Portfolio illustrant l'activité du laboratoire ESPACE,
fourni au HCERES le 15 juin 2022.

Didier Josselin

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To cite this version:

Didier Josselin. Portfolio illustrant l'activité du laboratoire ESPACE, fourni au HCERES le 15 juin 2022.. CNRS. 2022, https://www.umrespace.org/. hal-04350907

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# PORTFOLIO de l'UMR 7300 ESPACE

## CAMPAGNE D'ÉVALUATION 2022-2023

### VAGUE C

## INTRODUCTION

Ce portfolio illustre la richesse et la portée des productions du laboratoire ESPACE. Même s'il ne couvre pas exhaustivement l'activité de l'unité, il met en exergue des points saillants et seyants, représentatifs de notre identité. La sélection des sujets présentés a été réalisée en fonction d'une part, de critères issus du document d'auto-évaluation (DAE) et, d'autre part, d'éléments considérés par les membres d'ESPACE comme particulièrement importants ou caractéristiques de l'unité, notamment dans le cadre d'une UMR multisites.

Le choix des critères, tout comme celui des sujets présentés, a été réalisé de manière collégiale et constructive, lors d'une assemblée générale en présentiel, les 7 et 8 mars 2022.

Les critères retenus sont les suivants (sans hiérarchisation particulière) :

- La visibilité scientifique, via la catégorisation des publications dans le premier quartile du domaine thématique et/ou leur caractère international,

- Le caractère inclusif et structurant de l'activité de l'unité, avec notamment la formation et la participation de jeunes chercheur.se.s d'ESPACE, le caractère transversal (intersites) de la production,

- Le positionnement de la production ou de l'activité au front de la connaissance thématique ou méthodologique, ainsi que ses spécificités (interdisciplinarité par exemple),

- L'impact sur la société, par l'innovation sociale, la dissémination de la connaissance scientifique, l'aide à la décision et l'ancrage territorial.

Compte tenu de la taille de notre UMR (49 personnels permanents), 11 points saillants ont été sélectionnés et sont classés selon deux grands types : articles publiés (au nombre de cinq) ou activités développées au sein de l'unité. Ils sont extraits des différentes actions de recherche composant les 3 axes thématiques du laboratoire et les représentent de façon consistante et équilibrée, couvrant ainsi les objectifs scientifiques fixés dans le programme du contrat sexennal 2018-2023.

Les éléments du portfolio sont organisés par domaine d'activité, sans ordre de préférence particulier :

- Une UMR qui publie et qui innove : 5 articles scientifiques dans des revues internationales de renom

- Une UMR qui contribue à la vitalité et à la dynamique des interactions entre les scientifiques de son domaine : organisation de conférences (internationales) par ESPACE

- Une UMR qui éclaire et aide à la décision et à l'action : ancrage territorial et recherches appliquées
- Une UMR qui forme par et pour la recherche : développement marqué du lien entre la recherche et l'enseignement supérieur

- Une UMR qui vulgarise et dissémine ses connaissances vers la société : la place de la médiation scientifique à ESPACE

- Une UMR qui collecte, gère, traite et diffuse des informations scientifiques : supports de publication en libre accès, corpus et plateformes expérimentales de données

- Une UMR qui se préoccupe de la qualité de vie au travail : actions fonctionnelles transversales et structurantes à ESPACE.

Une synthèse des éléments présentés dans ce portfolio apparaît dans la Figure 1.

<table><caption>Figure 1. Positionnement des points saillants présentés selon les critères clés</caption><thead><tr><th>ID #</th><th>Type</th><th>Activité de l'UMR</th><th>Visibilité scientifique</th><th>Caractère inclusif et structurant</th><th>Positionnement principal du contenu</th><th>Impact sociétal</th></tr></thead><tbody><tr><td>1</td><td rowspan="5">Article scientifique</td><td rowspan="5">Publication, innovation</td><td rowspan="5">Q1 Internationale</td><td rowspan="3">Intersite Jeune chercheur</td><td>Thématique</td><td>Risque</td></tr><tr><td>2</td><td>Interdisciplinaire</td><td>Observatoire</td></tr><tr><td>3</td><td rowspan="2">Jeune chercheur</td><td>Méthodologique</td><td>Fabrique urbaine</td></tr><tr><td>4</td><td rowspan="2">Méthodologique, Interdisciplinaire</td><td>Planification urbaine</td></tr><tr><td>5</td><td></td><td>Santé</td></tr><tr><td>6</td><td rowspan="5">Activité scientifique</td><td>Organisation de conférences</td><td>Internationale</td><td>Intersite Jeune chercheur</td><td>Méthodologie, sciences régionales</td><td>Société numérique</td></tr><tr><td>7</td><td>Aide à la décision, à l'action</td><td>Internationale</td><td>Multisite</td><td>Méthodologie</td><td>Applications sur les territoires</td></tr><tr><td>8</td><td>Formation par et pour la recherche</td><td>Nationale</td><td>Intersite Jeune chercheur</td><td>Performance et adéquation des formations</td><td>Partenariat régional</td></tr><tr><td>9</td><td>Transfert des connaissances scientifiques</td><td>Nationale</td><td>Multisite Jeune chercheur</td><td>Médiation, vulgarisation scientifiques</td><td>Information appropriation</td></tr><tr><td>10</td><td>Collecte et diffusion des données scientifiques</td><td>Internationale</td><td>Intersite</td><td>Science ouverte</td><td>Information, diffusion, participation</td></tr><tr><td>11</td><td></td><td>Qualité de vie au travail</td><td></td><td>Intersite Transversal</td><td>Développement et cohésion internes</td><td>Qualité de l'activité scientifique</td></tr></tbody></table>

Type : article en revue internationale ou activité scientifique ; Activité de l'UMR : détaille le type d'activité ; Visibilité scientifique : classement en premier Quartile pour les articles, portée nationale ou internationale de l'activité ; Caractère inclusif et structurant : indique la participation de jeunes chercheurs, le caractère multisites (déploiement en parallèle sur les sites) ou intersites (transversal aux sites) des activités ; Positionnement principal : apport caractérisant l'élément (non exhaustif) ; Impact sociétal : domaines et niveaux d'application.
# UNE UMR QUI PUBLIE ET QUI INNOVE

## 5 ARTICLES sélectionnés

Les auteurs d'ESPACE sont indiqués en caractère gras avec leur statut et leur affiliation.

### #1 – ARTICLE

How wildfire risk is related to urban planning and Fire Weather Index in SE France
(1990-2013)

Fox D.M. (PR, Université Côte d'Azur), Carrega P. (PREM, Université Côte d'Azur), Ren Y. (stagiaire, ESPACE), Caillouet P. (stagiaire, ESPACE), Bouillon C., Robert S. (DR, CNRS)........4

### #2 – ARTICLE

A framework to analyse urban sprawl in the French Mediterranean coastal zone

Robert S. (DR, CNRS), Fox D. (PR, Université Côte d'Azur), Boulay G. (MCF, Avignon Université), Grandclément A., Garrido M., Pasqualini V., Prévost A. (post-doc, CNRS), Schleyer-Lindenmann A. (MCF, Aix-Marseille Université), Trémélo M.-L. (IE, CNRS)..................15

### #3 – ARTICLE

From the street to the metropolitan region: Pedestrian perspective in urban fabric analysis

Araldi A. (Post-doc, ESPACE), Fusco G. (DR, CNRS)..................................................32

### #4 – ARTICLE

Vacant lands on French Mediterranean coastlines: Inventory, agricultural opportunities, and prospective scenarios

Casanova Enault L. (MCF, Avignon Université), Popoff T. (Ingénieure, ESPACE), Debolini M. .. 54

### #5 – ARTICLE

Using Bayesian networks for environmental health risk assessment

Perez S. (MCF, Université Côte d'Azur), German-Labaume C., Mathiot S., Goix S., Chamaret P. ..................................................................................................................................................72
#1 - ARTICLE

HOW WILDFIRE RISK IS RELATED TO URBAN PLANNING AND FIRE WEATHER INDEX IN SE FRANCE (1990-2013)

Fox D.M. (PR, Université Côte d'Azur), Carrega P. (PREM, Université Côte d'Azur), Ren Y. (stagiaire, ESPACE), Caillouet P. (stagiaire, ESPACE), Bouillon C., Robert S. (DR, CNRS)

2018, *Science of The Total Environment*, Volume 621, 2018, Pages 120-129, ISSN 0048-9697

https://doi.org/10.1016/j.scitotenv.2017.11.174

https://www.sciencedirect.com/science/article/pii/S0048969717332333

Dans un contexte de réchauffement climatique, le risque d'incendie de forêt s'aggrave dans de nombreuses régions du monde. Au-delà des contextes climatiques et écologiques habituels, les incendies de forêt s'étendent vers des saisons et zones géographiques jusqu'ici peu touchées par les feux. Les incendies extrêmes de l'Australie en 2019 ainsi qu'en Californie depuis quelques années montrent bien l'effet catastrophique sur la santé humaine et les écosystèmes. En France, le feu de Gonfaron de 2021 dans le département du Var a brûlé près de 7000 ha, le troisième plus grand incendie de forêt depuis 1973.

Les incendies de forêt sont un risque naturel bien évidemment étroitement lié aux conditions météorologiques. Les différents paramètres (vent, température, humidité relative, état de sécheresse) sont intégrés dans une équation pour créer un indice météorologique de risque d'incendie de forêt, ou un *Fire Weather Index (FWI)*. Cependant, le risque d'éclosion d'un feu est aussi intimement lié à la configuration de l'habitat, notamment sa densité, car des maisons isolées ou diffuses dans le paysage représentent des interfaces habitats-forêt (*Wildland Urban Interface, WUI*) particulièrement propices aux incendies.

L'article publié dans la revue *Science of the Total Environment*<fnref n="1" /> quantifie l'évolution de l'habitat et du FWI entre 1990-2013 dans les Alpes-Maritimes et montre que, malgré une augmentation claire du risque météorologique, les superficies brûlées ont fortement diminué avec le temps. Le risque structurel d'un incendie de forêt lié à la configuration de l'habitat dans le paysage a évolué favorablement par l'augmentation de la densité de l'habitat. Des quartiers plus denses réduisent la propagation du feu et permettent une suppression de l'incendie plus rapidement. A cela s'ajoute l'impact de l'évolution de la stratégie du Service Départemental d'Incendie et de Secours (SDIS<fnref n="2" />) qui favorise une suppression rapide avant que le feu prenne de l'ampleur (l'Extinction Massive de Foyers de Feux Naissants).

Cette publication est représentative des travaux que nous essayons de mener au sein de l'UMR ESPACE par 3 caractéristiques : 1. il est pluridisciplinaire, intégrant des éléments de géographie physique (la climatologie) et de géographie humaine (les interfaces habitat-forêt) dans une perspective globale de la gestion des risques naturels et territoires ; 2. il rassemble 3 membres de 2 sites différents de l'UMR avec des compétences spécifiques (Fox : évolution de l'occupation du sol ; Carrega : climatologie ; Robert : aménagement urbain) ; 3. il est le résultat d'une collaboration avec l'IRSTEA (maintenant INRAE<fnref n="3" />) qui représente notre capacité à créer des collaborations et réseaux de partenariats de recherche. Enfin, deux des auteurs (Ren & Caillouet) sont des étudiantes stagiaires qui ont été intégrées parmi les auteurs en reconnaissance d'une contribution réelle à la publication.

La revue *Science of the Total Environment* est une revue d'excellence (IF 8.0) qui publie des articles à l'interface de différentes disciplines. L'article en question a déjà été cité 26 fois depuis 2018 (environ 8 fois par an), attestant ainsi de son intérêt dans la communauté scientifique.

<footnote n="1">¹ https://www.journals.elsevier.com/science-of-the-total-environment</footnote>

<footnote n="2">² http://fransylva-paca.fr/wp/services-departementaux-dincendie-et-de-secours/</footnote>

<footnote n="3">³ https://www.inrae.fr/centres/provence-alpes-cote-dazur</footnote>
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ELSEVIER

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# How wildfire risk is related to urban planning and Fire Weather Index in SE France (1990–2013)

<figure><img src="image_5.png" /></figure>

Check for
updates

D.M. Fox a,*, P. Carrega a, Y. Ren a, P. Caillouet a, C. Bouillon b, S. Robert c

## HIGHLIGHTS

* Land use planning in SE France led to increased housing density in 1990-2012 and maintained WUI wildfire risk constant.

* The number of buildings increased by 30% but WUI area increased by less than 5% as housing density increased.

* Fire frequency and burned area increase substantially as Fire Weather Index surpasses a threshold value of 90.

## GRAPHICAL ABSTRACT

<figure><img src="image_6.png" /></figure>

## ARTICLE INFO

**Article history:**

Received 28 September 2017

Received in revised form 15 November 2017

Accepted 15 November 2017

Available online xxxx

Editor: D. Barcelo

**Keywords:**

Land use planning

Wildfires

Forest fires

Wildland Urban Interface (WUI)

Fire Weather Index (FWI)

## ABSTRACT

Wildfires burn >450,000 ha of forest every year in Euro-Mediterranean countries. Many fires originate in the Wildland Urban Interface (WUI) where housing density and weather conditions affect fire occurrence. Housing density is determined by long term land use policies while weather conditions evolve quickly. The first objective was to quantify the impacts of land use policy on WUI characteristics and fire risk in SE France during 1990–2012. The second objective was to quantify how Fire Weather Index (FWI) is related to fire occurrence. WUI was mapped from 1990, 1999, and 2012 building layers and crossed with a NDVI derived vegetation layer. In all, 12 WUI categories were derived: 4 building density classes and 3 vegetation layers. The 187 FWI was based on daily temperature, wind speed, relative humidity and soil water content. Despite a 30% increase in the number of new buildings, WUI area increased by only 5% as new housing filled in open space in existing WUI area. This trend can be linked to national level urban planning legislation and forest fire protection laws. Major driver variables determining housing location were aspect, slope, and distance to city centers. Fire frequency and burned area were nonlinearly related to FWI: 73% of the 99 fires occurred during weeks with FWI values ≥90 even though these accounted for only 44% of all weeks. Burned area was even more sensitive to FWI since 97% of total burned area occurred during weeks with mean FWI values ≥90. All days with burned areas >100 ha had FWI values >150. The study demonstrated that WUI legislation can be an efficient tool to limit WUI fire risk. FWI results suggest the predicted increase in extreme summer heat events with global warming could increase burned area as firefighting resources are stretched beyond capacity.

© 2017 Elsevier B.V. All rights reserved.

<footnote>a UMR 7300 ESPACE CNRS, Université Côte d'Azur, France</footnote>

<footnote>b IRSTEA, Aix-en-Provence, France</footnote>

<footnote>c UMR 7300 ESPACE CNRS, Université d'Aix-Marseille, France</footnote>

<footnote>* Corresponding author.
E-mail address: fox@unice.fr (D.M. Fox).</footnote>
# 1. Introduction

Forest fires in the Euro-Mediterranean Wildland Urban Interface (WUI) result from complex interactions between weather, WUI characteristics, and fire-fighting strategies (fire prevention and suppression) (San-Miguel-Ayanz et al., 2013). Although fire frequency and burned area are expected to increase with global warming (Lozano et al., 2016; Moriondo et al., 2006), this has not been a consistent trend in recent decades. For example, despite warmer summer temperatures, SE France experienced a sharp drop in fire frequency and burned area since the early 1990's due to more efficient fire prevention/suppression (Fox et al., 2015). Based on data published in the Joint Research Center 2014 report (JRC, 2014), most fire-prone Euro-Mediterranean countries (Portugal, Spain, France, Italy, and Greece) have experienced fewer fires in the past decade. Fire frequency increased rapidly in the 1990's and early 2000's as summer temperatures broke heat records, especially in Portugal, Spain and Greece, but it has decreased over the past 10 years and burned area also shows an overall declining trend (JRC, 2014). Since the 2004–2013 decade was the warmest on record in Europe (https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature-4/assessment, consulted 31 Oct. 2017), weather conditions alone do not explain the evolution in fire occurrence. WUI characteristics and fire prevention and suppression are major factors to be taken into consideration in addition to fire weather. These trends underscore the need to study fire occurrence in a context of interacting and evolving weather and land cover characteristics, as suggested by Vilar et al. (2016). And fire-WUI and fire-weather relationships are valid only within stable fire prevention and suppression efforts (Fox et al., 2015).

Of the three components cited above (WUI characteristics, weather, and prevention/suppression), weather is naturally occurring and beyond the control of land managers. It is also the most dynamic of the three, as weather related fire risk can evolve over days to hours. Temperature, rainfall and relative humidity are major weather variables but are not enough to account for burned area since big fires need strong winds, so Fire Weather Indices (FWI) are better measures of weather-related fire risk than temperature or drought conditions alone (Carrega and Jeronimo, 2007). A number of indices have been developed to estimate weather-related fire risk: the Canadian Fire Weather Index which is used today in different countries, Mc Arthur's Fire Danger Meters, the Italian Fire Danger Index, the Portuguese Method, and Carrega's meteorological fire indices I<sup>85–90</sup> and I<sup>87</sup> (Arpaci et al., 2013; Bedia et al., 2015). Several indices were compared and tested on large European summer fires during the 1991–1995 interval in the context of the MEGAFiReS European research project (Bovio and Camia, 1998). The I<sup>87</sup> was developed for Mediterranean environments and tested specifically in SE France where it has been proven successful (Carrega, 1991). The index (described below) includes temperature, relative humidity, wind speed, and a surrogate measure of vegetation moisture content. Since the number of extreme days is increasing in southern France (Cardil et al., 2014), relating fire risk to weather variables is a critical step in identifying high risk periods.

Fire-fighting strategy depends on risk management authorities and budget allocation, but in extreme conditions, even highly prepared and equipped firefighters can be overwhelmed by a great number of ignitions. Once a fire escapes initial suppression, it can become unstoppable in extreme heat, vegetation moisture, and wind, until weather conditions improve (Moritz et al., 2010). In general, fire prevention and suppression have contributed to decrease fire frequency and burned area in recent years in many areas (Fox et al., 2015; Pezzatti et al., 2013).

WUI characteristics evolve slowly over a period of decades as land cover, developed area, and housing density change. Different criteria and scales can be used to characterize the WUI interface. These are reviewed in Galiana-Martin et al. (2011) and generally include land use/cover, urban and suburban densities, and vegetation dynamics. In

SE France, the greatest fire risk is associated with isolated, then scattered housing, and fire risk decreases with increasing housing density (Lampin-Maillet et al., 2010; Lampin-Maillet, 2009). Despite the potential for more frequent fire ignitions in greater WUI housing densities, vegetation biomass and continuity decrease and the probability of extinguishing a fire early increases. Low housing density has also been identified as a factor contributing to structure damage by wildfires in California (Syphard et al., 2014). Housing density is regulated at the municipal level through building permits, but external land use policies orient municipal decisions in France (Prévost and Robert, 2016). The first is comprised of national-level laws (Solidarity and Urban Renewal (SRU) law, 2000; and the ALUR law of 2014) that incite municipalities to increase housing density and favor urban renewal over urban and suburban expansion.

Since the national Barnier Risk Prevention Plan law of February 1995, French municipalities are obliged to elaborate Risk Prevention Plans that take into account the dominant risks impacting their territories. In SE France, wildfires are one of the most frequent risks, so an extensive campaign has been undertaken to develop appropriate fire prevention plans. These documents, comprised mainly of fire risk maps, constrain municipal urban planning documents. Large tracts of forest can be classed as non-developable to avoid increasing the number of isolated and scattered houses and WUI area. Traditionally, the climatic and natural landscape amenities make isolated and low density housing a favored form of residential and secondary housing in the region. Although the Risk Prevention Plan law was promulgated in 1995, many municipalities have resisted state efforts to implement risk plans because the freezing of forest land represents a loss in potential residential taxes.

In a long term study (about 1960–2009), Fox et al. (2015) demonstrated that summer temperatures and WUI fire risk increased substantially with the socio-economic development of the Côte d'Azur region of SE France. The study did not take into account the impacts of land use policies aimed at increasing housing density and reducing fire risk since these were introduced only in the latter phase of the time interval studied. The study also indicated that forest fire prevention and suppression strategies intensified around 1990 after several major fires in the 1980's, but these have changed little since then, so fire risk in the time interval studied here is limited to WUI and weather factors. The first objective of this paper is to quantify how the WUI has evolved since 1990 in the light of the planning restrictions described above and how this has affected WUI fire risk in the 1990–2013 interval. In addition, the study will examine how FWI values have evolved and how these are related to fire frequency and burned area. The novelty of our approach is examining temporal trends that account for changes in the WUI in conjunction with fire weather variables in a context of stable fire prevention/suppression. The following elements will therefore be analyzed: changes in WUI configuration and WUI fire risk index, summer FWI values, and fire characteristics (frequency and burned area). We believe the results of this particular case study suggest that land use policies can effectively limit WUI fire risk in the Mediterranean and other fire-prone zones.

# 2. Methods

## 2.1. Site description

The study area is located in SE France and is comprised of 2 sub-zones - West and East (Fig. 1). Together (about 425 km²), they make up about half the total WUI area of the Alpes-Maritimes department. To the north, the distance from the coast and steep topography of the Alps limit development. Along the coast, the high density cities of Cannes, Nice and others have too little vegetation to be included in the WUI zone. Vegetation is similar in the two study zones. Dominant species are typical of Mediterranean areas and are generally highly inflammable (aromatic and resinous species). Dominant tree species
<figure><img src="image_7.png" /><figcaption>**Fig. 1.** The West (168 km²) and East (257 km²) WUI zones are located in the Alpes-Maritimes department of SE France near the cities of Cannes and Nice.</figcaption></figure>

are a mixture of pines (*Pinus halepensis*) and oaks (*Quercus ilex*, and *Quercus Pubescens*). Shrubs include *Cistus albidus*, *Cistus monspeliensis*, *Genista cinerea*, *Spartium junceum*, *Calicotome spinosa*, *Lavandula latifolia*, and *Thymus vulgaris*.

Topographic and socio-economic characteristics differ slightly between the two zones. The West has a mean altitude of 302 m, and mean and median slopes of 14.8° and 11.6°, respectively. Topography is steeper in the East with a mean altitude of 492 m and mean and median slopes of 23.6° and 23.2°. Human settlement patterns are also slightly different. The West zone is located North of Cannes and contains a number of intermediate-sized cities. Among these we can cite the towns associated with Sophia Antipolis, a high tech industrial zone, and the city of Grasse with its perfume manufacturing industry. After tourism, Sophia Antipolis is the second major source of economic activity in the Alpes-Maritimes department while the perfume industry in Grasse is the third. The East zone, situated just north of Nice is more residential with small towns and villages aligned in narrow valleys and/or located on steep slopes. In the East, only Nice has significant employment potential. The West therefore has gentler topography and more employment opportunities than the East. The West and East zones reflect typical peri-urban WUI configurations found throughout much of the Mediterranean basin: a suburb dependent on a large city (East), and an agglomeration of smaller viable cities (West). Neither zone has any agricultural-wildland interface.

## 2.2. Wildland Urban Interface (WUI) characteristics

Mapping WUI housing density and calculating WUI fire risk was derived from Lampin-Maillet and Bouillon (2011) and Bouillon et al. (2014) and modifications are described in Fox et al. (2015). An initial

building vector data layer was extracted from the BD-TOPO© database, a vector database produced by the National Geographic Institute (IGN). This 2008 building layer was updated to 2012 by overlaying it on 2012 color orthorectified aerial photographs (0.5 m resolution) and screen digitizing new buildings and deleting the few buildings that were destroyed between 2008 and 2012. This building vector layer was then consecutively overlain on 1999 color and 1990 panchromatic orthorectified aerial photographs (0.5 m resolution) and buildings were deleted (mostly) and digitized (occasionally) as we went back in time. This procedure generated three comprehensive individual building layers for the East and West zones for 1990, 1999, and 2012; for each date, the number of buildings and building footprint area were quantified.

Using the RUmap ArcGIS-based model (Lampin-Maillet and Bouillon, 2011; Bouillon et al., 2014), building layers were converted to housing density based on the number of buildings within coalesced 100 m buffer zones around buildings. In all, 4 suburban categories were defined: Isolated, Scattered, Dense and Very Dense. RUmap spatial resolution output is 2.5 m.

Vegetation was mapped from 2009 infrared (IR) orthorectified aerial photographs (initial resolution 0.2 m downscaled to 0.5 m) based on NDVI indices. NDVI thresholds used to distinguish between sparse (mostly bedrock), discontinuous, and continuous vegetation were the following: <0.01, 0.01 to <0.15, and ≥0.15, respectively. These were set visually by comparing category distributions to the high resolution aerial photographs. In the absence of high resolution IR images for earlier years, only one vegetation map was used for the 3 dates, so vegetation cover was slightly underestimated for 1990 and 1999 in built areas.

The housing density and vegetation layers were upscaled to 10 m to match the Digital Elevation Model (DEM) and overlain to create 12 WUI
<table><caption>**Table 1**
Category fire risk indices for each WUI category.</caption><thead><tr><th rowspan="2">WUI</th><th colspan="3">Vegetation</th></tr><tr><th>Sparse</th><th>Discontinuous</th><th>Continuous</th></tr></thead><tbody><tr><td>Isolated</td><td>7.5</td><td>10.0</td><td>7.5</td></tr><tr><td>Scattered</td><td>3.5</td><td>4.5</td><td>6.5</td></tr><tr><td>Dense</td><td>2.0</td><td>3.0</td><td>3.0</td></tr><tr><td>Very Dense</td><td>1.5</td><td>2.0</td><td>3.0</td></tr></tbody></table>

categories (4 housing × 3 vegetation categories). Land areas outside the WUI (i.e. zones of natural vegetation without buildings) were ignored in the WUI fire risk calculation. Fire risk indices are shown in Table 1 for the 12 categories, and these were attributed according to results of Lampin-Maillet (2009) and Lampin-Maillet et al. (2010) and adapted by Fox et al. (2015). The indices take both fire frequency and burned area in SE France into account. For the study zone, WUI fire risk is calculated using Eq. (1).

$$ \text{Fire risk} = \sum (\text{Category index} \times \text{Category area}) / \text{Study area} \quad (1) $$

Driver variables affecting WUI changes included the following topographic and distance variables: altitude, aspect, slope, and distances from the coast, city centers, and roads. However, only aspect, slope, and distance from city centers were retained for this article. These variables show trends that are probably typical of much of the Euro-Mediterranean region. Building density and changes in building density over time decrease exponentially with distance from main roads, and this too is probably applicable throughout the Mediterranean. However, since these trends are straightforward, the graphs were not included here to save space. Finally, altitude and distance from the coastline were considered site specific with trends that probably could not be generalized as easily to other Mediterranean regions.

## 2.3. Fire Weather Index (FWI)

Eq. (2) shows the $I^{87}$ FWI calculation, where T is Air Temperature (°C), V is Wind Speed (m s⁻¹), Rₚ is Deep Soil Water Reserve (mm), Rₛ is Superficial Soil Water Reserve (mm), and H is Relative Humidity (%). The $I^{87}$ FWI requires daily data at fine resolution time steps, so T, V, and H were selected at 12:00 UTC for each day, and Rₚ and Rₛ were calculated daily using a decreasing exponential equation to empty soil water reserve from potential evapotranspiration (Carrega, 1991). Inversely, rainfall was added to soil water content which is correlated with vegetation water content during the vegetative season (spring and summer). The index was calculated for a weather station (Mandelieu) located in the west zone. Daily $I^{87}$ FWI values were calculated for the months of July and August in the 1993–2013 interval (1993 being the first year these data were available).

$$ I^{87} = (T V) / (R_p^{0.5} R_s H) \quad (2) $$

## 2.4. Number of fires and burned area

A complete fire record of all fires occurring in vegetation patches >1 ha is available since 1973 in the national Prométhée database (http://www.promethee.com). All fires >1 ha occurring in the estimated WUI zone of the Alpes-Maritimes department were selected for the months of July and August in the 1993–2013 interval to correspond with the WUI and FWI data interval. Fire frequency and burned area statistics were calculated from these data.

In summary, WUI characteristics correspond to available aerial photographs and were calculated for the years 1990, 1999, and 2012. FWI values and fire statistics were recorded at daily time-steps for the

months of July and August during the 1993–2013 interval, and these were then treated at annual, monthly and weekly scales.

## 2.5. Statistical analysis

Effects of driver variables (Aspect, Slope, Distance from city centers) on building area and growth rates during the 1990–1999 and 1999–2012 time intervals were tested using Analysis of Variance where categories within driver variables were explanatory variables and building area/growth rates were dependent variables.

Differences in FWI, fire frequency and burned area between the months of July and August were also tested using Analysis of Variance. Fire frequency and burned area were normalized using Log₁₀ and square root transformations before running the ANOVA. Log₁₀ and square root values of fire frequency and burned area were also used to identify critical threshold values of FWI indicating a jump in fire risk. FWI threshold values of 80, 85, and 90 were tested.

# 3. Results

## 3.1. WUI characteristics

Building numbers and drivers will be described before addressing WUI housing densities.

### 3.1.1. Building numbers and drivers

Number of buildings and building growth rates are similar for the two zones with slightly more buildings in the West than in the East: 30,793 versus 26,710 in 2012. Both zones have slightly greater building growth rates in the earlier (1990–1999) interval compared to the latter (1999–2012) period: 338 bldgs/yr versus 316 bldgs/yr in the West, and 279 bldgs/yr versus 268 bldgs/yr in the East. When surface area is taken into account, building density is greater in the West than in the East: 183 bldgs/km² versus 104 bldgs/km², respectively (in 2012), due to the more favorable topographic and socio-economic conditions described above. Similarly, growth rates per unit area are greater in the West at 2.01 bldgs/yr/km² than in the East (1.23 bldgs/yr/km²). Overall, between 1990 and 2012, the number of buildings increased by 30.3% and 28.9% in the West and East, respectively (average total growth of 29.6%).

Building number trends with respect to driver variables show similar tendencies in the West and East, so values for the two zones were grouped into common figures. Units for analyzing building drivers are based on the building footprint where the area occupied by buildings is quantified without sidewalks, roads, parking lots... Aspect strongly influences building area ($R² = 0.99, P ≤ 0.001$) and growth rates ($R² = 0.99, P ≤ 0.001$) (Fig. 2). The percentage of area occupied by buildings is about 3 times greater on South-facing slopes than North-facing (Fig. 2a). East and West-facing slopes have intermediate values. Growth rates (Fig. 2b) are almost 5 times slower on North-facing slopes compared to South-facing slopes and about 3 times slower on East and West-facing slopes. Building area values are statistically different for the 3 years considered ($P ≤ 0.05$), and although growth rates are slightly lower in the 1999–2012 interval, these are not statistically significant.

Slope inclination plays a major role in building location ($R² = 0.99, P ≤ 0.001$) and growth rates ($P ≤ 0.05$) over time (Fig. 3). The percentage of area built decreases exponentially with slope (Fig. 3a) for all years with decreases of close to 50% with each increase in slope interval. The change in growth rate, however, is only about 17% less when passing from the first (0–15°) to the second (15–30°) intervals (Fig. 3b). This suggests the gentlest slopes had little space left for development and steeper slopes continued to be colonized at close to the original rate. The drop is then sharp (about 60%) for the third slope category (30–45°), and rates continue to be lower at all slope angles during the second time interval. Building area per slope varies between the different
<figure><img src="image_8.png" /><figcaption>**Fig. 2.** a: Percentage of area built in 1990, 1999, and 2012 according to aspect. b: Change rates in area built according to aspect in 1990–1999 and 1999–2012.</figcaption></figure>

years ($P \le 0.05$), but growth rates are not statistically significant for the two time intervals shown in Fig. 3b, though the latter period shows slightly slower growth.

Distance from city centers is also statistically significant for both building area ($R^2 = 0.99, P \le 0.001$) and growth rates ($P \le 0.001$). As expected, built area is greatest close to city centers and decreases rapidly beyond a distance of 500 m; it changes little beyond a distance of about 2000 m (Fig. 4a). Growth rates (Fig. 4b) show a more complex pattern: close to city centers (0–500 m), values are low for both time intervals since there is little space left to develop. They remain high, however, for both successive distance intervals (500–1000 m and 1000–1500 m) before decreasing linearly with distance to about 3500 m. Temporally, growth rate drops sharply with time (36% decrease) in the 0–500 m distance range, but drop rates slow with successive distance intervals with values of 22% and 6% for 500–1000 m and 1000–1500 m, respectively. As city centers developed and were saturated, demand for land remained high in the closest outer ring. As for the preceding explanatory variables, building area values show statistically significant differences between

<figure><img src="image_9.png" /><figcaption>**Fig. 3.** a: Percentage of area built in 1990, 1999, and 2012 according to slope. b: Change rates in area built according to slope in 1990–1999 and 1999–2012.</figcaption></figure>

the 3 years ($P \le 0.001$), but change rates are not statistically different for the 2 time intervals despite slightly slower rates in the 1999–2012 interval.

### 3.1.2. WUI categories

Total WUI area by housing density category is shown in Fig. 5a. Dense and Very Dense housing account for the greatest area within the WUI, and total WUI area is 214.7 km², 220.6 km², and 225.3 km² for 1990, 1999, and 2012, respectively. The overall 1990–2012 areal growth rate of 4.9% contrasts sharply with the building growth rate of almost 30% described above; the number of buildings grew much faster than the WUI area, suggesting that housing density increased over time
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** a: Percentage of area built in 1990, 1999, and 2012 according to distance from city centers. b: Change rates in area built according to city centers in 1990–1999 and 1999–2012.</figcaption></figure>

instead of expanding into new previously unbuilt areas. The shift to greater housing densities is clearly shown in Fig. 5a: Isolated, Scattered and Dense housing areas all decreased slightly over time while the Very Dense category gained area. These trends are confirmed in Fig. 5b, where only the Very Dense category has positive growth values and all other categories lose territory in all time intervals (1990–1999, 1999–2012, and 1990–2012). The greatest shift is from Dense to Very Dense housing.

<figure><img src="image_11.png" /><figcaption>**Fig. 5.** a: Total (West & East) built area in 1990, 1999, and 2012. b: Change in total (West & East) built area over time in 1990–1999 and 1999–2012.</figcaption></figure>

### 3.1.3. WUI risk evolution

The WUI fire risk for each zone depends on the areal distribution of housing densities and their evolution over time. For the West, fire risk values are 1.89, 1.89, and 1.87 for 1990, 1999, and 2012, respectively. For the East, they are 1.85, 1.83, and 1.82. Despite a 30% increase in the number of buildings and a 5% gain in WUI area, the fire risk remains stable for both zones due to a shift from high risk low density categories to low risk Very High densities.

How the fire risk is distributed at any time period depends on the spatial area of the category and risk index associated with each category, and risk distributions vary with study zone and time (Fig. 6a–b). In the West (Fig. 6a), most of the risk in 1990 is accounted for by the Dense-continuous vegetation category (about 25% of 1990 risk) and then the Very Dense category (about 22% of risk). In the East (Fig. 6b), the 1990 peak is in the Scattered-continuous (about 24% of risk) and Isolated-continuous (about 17% of risk) categories. These differences reflect the topographic and employment conditions of the West and East zones, respectively.
<figure><img src="image_12.png" /><figcaption>**Fig. 6.** a: WUI risk distribution in West in 1990. 1999, and 2012. b: WUI risk distribution in East in 1990. 1999, and 2012. c: Total WUI risk distribution in 1990. 1999, and 2012. d: Change in total WUI fire-risk distribution in 1990–1999 and 1999–2012.</figcaption></figure>

Temporally, both zones show the strongest increases in WUI fire risk in the Very Dense category, and in the West (Fig. 6a), Very Dense-continuous vegetation accounts for the greatest part of the fire risk (about 32%) in 2012. In the East (Fig. 6b), the peak risk remains in Scattered-continuous in 2012 (about 22%), but Very-Dense continuous comes second (about 19%), ahead of Isolated-continuous.

The total WUI fire risk is a compromise between the West and East zone distributions, as shown in Fig. 6c, where peak risk always occurs in continuous vegetation and where Dense and then Scattered housing densities account for most of the fire risk. With time, the risk shifts to denser housing categories and decreases with housing density within the continuous vegetation category. All vegetation categories gain in risk (final % risk – initial % risk) in the Very Dense housing category and the gain is slightly greater during the second time interval (1999–2012) (Fig. 6d).

## 3.2. Fire Weather Index (FWI), fire frequency and burned area

Temporal changes in the FWI and fire occurrence will be analyzed at the weekly, monthly and annual scales. In all, 99 fires >1 ha burned a total area of 3908 ha. At the weekly scale (Fig. 7), FWI (Fig. 7a) increases progressively over the summer as vegetation dries with the cumulative lack of rainfall and warm summer temperatures. Strangely, fire frequency and burned area (Fig. 7b) do not entirely reflect FWI patterns. Burned area generally depends on a few large fires and in this case, a single fire of 1595 ha (5 Aug., 2003) accounts for more than half the burned area of the peak in week 6. The number of fires peaks around mid-August and this corresponds to high FWI values. However, the first week of July has the lowest FWI values of the summer (Fig. 7a) but a high fire frequency (Fig. 7b), the second peak after mid-August. Similarly, the FWI value continues to increase right into early September while the
<figure><img src="image_13.png" /><figcaption>**Fig. 7.** a: Mean FWI values according to summer week (1 = first week of July; 9 = last week of August). b: Fire frequency (sum) and total burned area (sum) according to summer week.</figcaption></figure>

number of fires drops. These inconsistencies will be taken up again in the Discussion.

Fig. 8 plots fire frequency and burned area against mean weekly FWI for the 189 weeks of the study period. The relationship is clearly not linear since a number of high FWI value weeks have no fires. However, only weeks with mean FWI values >90 have >2 fires per day, and 72.7% of the 99 fires occur during weeks with FWI values >90 even though these account for only 43.9% of all weeks. Of the different thresholds tested (80, 85, and 90) for normalized values (Log₁₀ and Square Root) of fire frequency and burned area, the threshold value of 90 gave the best explained variance. Burned area is even more sensitive to FWI since 96.8% of total burned area occurs during weeks with mean FWI values >90. Normalized values (Log₁₀) of the number of fires per week and burned area beneath and below the threshold value of FWI = 90 are statistically significantly different (P < 0.001, R² = 0.21 and 0.38, respectively).

<figure><img src="image_14.png" /><figcaption>**Fig. 8.** Weekly number of fires and burned area versus weekly FWI.</figcaption></figure>

Differences in FWI values and fire characteristics (Table 2) are coherent and reflect local summer fire dynamics. FWI values, fire frequency and burned are all greater in August than in July. FWI values increase by about 32% between July and August (both month and year are statistically different at P < 0.05, R² = 0.89). The number of days above a FWI value of 90 increases by about 71% between July and August. Fire frequency and burned area increase by 54% and 718%, respectively; differences between years are statistically significant (P < 0.05, R² = 0.78) for normalized (square root) values of fire frequency, but not for any of the normalized values of burned area, and differences between months are not statistically different (P < 0.05) for any normalized values of fire frequency or burned area. Despite the lack of statistical differences, some monthly tendencies are worth noting. Of the 99 fires >1 ha recorded, 60 (60.6%) occur in August, and of the 3908 ha burned, >3482 ha (89.1%) were due to fires in August. Only 6 days have burned areas >100 ha (accounting for about 79% of total burned area), and 5 of these (83.3%) occur in August. FWI values for these 6 days are all >150.

At the annual time scale, there are no signs of increasing FWI values or fire occurrence with time (Fig. 9). The greatest mean FWI values are generally around the peak 2003 year (Fig. 9a), but inter-annual variability remains high. Apart from the 1994 anomaly, where fire frequency and burned area are high (Fig. 9b) but FWI is only moderate (Fig. 9a), fire frequency and burned area are moderately correlated with FWI values: Pearson correlations between July fires and burned area with FWI are 0.42 and 0.58, respectively. Values for August are 0.48 and 0.65, respectively.

## 4. Discussion

Despite strong growth in the number of buildings and a local preference for South-facing residential villas, there is a clear shift towards increased housing densities in the Alpes-Maritimes WUI. This reflects tighter land use policies aimed at limiting expansion of low density housing into unbuilt forests. Hence WUI fire risk has remained constant

<table><caption>**Table 2**
FWI, number of fires and burned area at the monthly time scale.</caption><thead><tr><td>Month</td><td>Mean FWI</td><td>Days with FWI > 90</td><td>Number of fires</td><td>Burned area (ha)</td></tr></thead><tbody><tr><td>July</td><td>76.3</td><td>145</td><td>39</td><td>425.6</td></tr><tr><td>August</td><td>100.9</td><td>248</td><td>60</td><td>3482.5</td></tr></tbody></table>
<figure><img src="image_15.png" /><figcaption>**Fig. 9.** a: Mean FWI values by month and year. b: Fire frequency and burned area distributions by year.</figcaption></figure>

despite the construction of 13,145 new buildings (29.6% growth in building numbers). This clearly demonstrates the effectiveness of limiting fire risk through urban planning policies. WUI area is significantly related to fire occurrence for Portugal, Spain, France and Italy (Modugno et al., 2016; Vilar et al., 2016), so the results found here present a realistic strategy for all Euro-Mediterranean countries. Badia et al. (2011) suggest that WUI fires extend well beyond the Mediterranean and occur throughout the world, especially in developed countries. The main drivers affecting housing location in our study zone were aspect, slope, and distance from city centers. Although not shown graphically in this paper, distance from main roads was also a major driver of building growth as noted above.

Mann et al. (2014) predict an increase in housing density from low to medium density and an increase in total WUI area over time (2000–2050) in California. Syphard et al. (2007) demonstrate the impact of WUI on fire frequency, and to a lesser extent on burned area, so increased WUI area will probably be translated into greater fire occurrence. Mann et al. (2014) estimate that the number of houses

in high and very high wildfire risk zones will increase by about 50% over the 2000–2050 time interval, so unlike Mediterranean France, WUI is expected to continue to grow over the next decades in California. A modeling study based on different land use planning scenarios in California (Syphard et al., 2013) confirms that increasing housing density can have a substantial impact on fire risk in the WUI, so results from this study can be extended beyond the Mediterranean basin. However, despite the effectiveness of land use policy in controlling WUI fire risk in SE France, cultural reactions to centrally imposed government legislation may not make this solution acceptable in a North American context.

In addition to limiting WUI fire risk, greater housing densities are probably easier to protect and require fewer resources, so fewer firefighters need to be mobilized to defend a greater number of buildings and people. This becomes critical in extreme weather conditions where firefighters have to deal with a large number of ignitions and resources are spread among numerous ignitions or fire clusters (San-Miguel-Ayanz et al., 2013). Southern Europe has shown an increase in the number of extreme temperature days since 1978, so we can expect more days surpassing high risk FWI thresholds in the future. Better urban planning is one solution to mitigating wildfire risks due to climate change (Cardil et al., 2014). In addition, Syphard et al. (2014) demonstrated the effectiveness of creating defensible space around structures (reducing woody cover close to structures and ensuring vegetation does not touch structures), and higher density housing reduces the defensible area necessary to protect homes – less area protects more structures.

Temporal trends in weekly fires show a complex pattern where FWI increases progressively while fire frequency and burned area peak in mid-August and again in early July. The relationship is not improved when fire frequency and burned area are compared to the number of days above a FWI value of 90 within each week. The abnormal peak in fires in the first week of July may be related to accidental ignitions from garden fires used to burn vegetation residues. Officially, the last day of June marks the end of these fires but some residents may continue burning into the first days of July, thereby provoking accidental fires. The weak correlation between FWI and fire occurrence in late August may be due to the number of tourist-related ignitions, but since these are not quantified there are no data to test this hypothesis. Human population densities and activities and fire weather all influence fire frequency, but their nonlinear nature makes it difficult to establish simple relationships (Syphard et al., 2007; Moritz et al., 2010).

In the absence of changes in WUI fire risk, and in a context of stable fire prevention and suppression efforts, fire-related weather becomes the only variable to account for fire dynamics in the study zone. The threshold FWI value of 90 marks a clear transition to much greater fire risk, particularly for burned area, and this is valid at both the weekly (when all weeks are analyzed independently without a summer temporal trend) and monthly scales. The threshold will vary from site to site and between indices. San-Miguel-Ayanz et al. (2013) also observed critical thresholds in FWI values using the Canadian FWI for large fires in Portugal. This is particularly relevant in a context of climate change where the frequency of extreme heat/drought events is expected to increase, in addition to longer drier summers (Lindner et al., 2014; Lozano et al., 2016; Moriondo et al., 2006).

Finally, the combination of WUI and FWI results indicate that fire prevention strategies can be more refined when both WUI and FWI are considered together. In the Euro-Mediterranean zone, south-facing slopes can be expected to have a greater number of high FWI days due to their greater exposure to incoming solar radiation. These slopes are also preferential areas for WUI development as described above. In practice, no difference is made between north facing and south facing slopes in terms of biomass control or preventive measures. However, given the greater WUI and fire weather risks, preventive measures (vegetation clearing, fire hydrant deployment...) should be developed to target these slopes more specifically.
## 5. Conclusions

Land use policy legislation in France has evolved over the past decades to favor increased housing density over suburban sprawl. Both national level laws and more Mediterranean-specific fire risk prevention legislation have significantly altered the evolution in housing density in SE France: between 1990 and 2013, building numbers increased by almost 30% but WUI built area increased by only about 5%. Isolated, Scattered, and Dense residential housing all declined in area while Very Dense housing increased. The WUI forest fire risk therefore shifted to this category over time, so the overall risk has remained constant despite the increase in new buildings. Land use planning is therefore an effective tool in managing forest fire risk in the Mediterranean context and beyond.

Three factors contribute to forest fire risk: WUI fire risk, fire prevention and suppression, and fire-related weather. In SE France, two of these, WUI risk and prevention/suppression, have remained constant over the past 2 decades, so inter-annual variations in fire frequency and burned area depend on weather. Fire frequency and burned area do not increase linearly with the $I^{87}$ FWI, but fire risk jumps when FWI values surpass a threshold of about 90. No temporal trend in FWI was observed since 1993, but the time interval is too short to reveal medium to longer term climate change trends.

## Acknowledgements

Y. Ren benefitted from an EMMA scholarship for which the authors express their gratitude. The authors are also grateful for the financial support provided by the Université Côte d'Azur for the acquisition of the historical orthorectified aerial photographs.

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#2 - ARTICLE

A FRAMEWORK TO ANALYSE URBAN SPRAWL IN THE FRENCH MEDITERRANEAN COASTAL ZONE

Robert S. (DR, CNRS), Fox D. (PR, Université Côte d'Azur), Boulay G. (MCF, Avignon Université), Grandclément A., Garrido M., Pasqualini V., Prévost A. (post-doc, CNRS), Schleyer-Lindenmann A. (MCF, Aix-Marseille Université), Trémélo M.-L. (IE, CNRS)

2019, *Regional Environmental Change*, Volume 19 (2), pages 559-572

https://www.doi.org/10.1007/s10113-018-1425-4

Cet article rend compte des compétences et de l'effort de recherche développé à ESPACE sur les modalités d'implantation et d'évolution de l'urbanisation côtière en Méditerranée. Sur les littoraux, la connaissance des ressorts de l'urbanisation reste encore trop sectorielle et trop générale, fondée sur le rôle de la distance à la mer, le niveau d'accessibilité des territoires, l'augmentation des mobilités, l'héliotropisme, etc. Elle n'est guère porteuse d'enseignements pour l'action publique, en particulier à l'échelle locale où tout se joue. Appréhender ce phénomène multidimensionnel requiert donc une approche systémique, multiscalaire et pluridisciplinaire.

L'article expose un cadre d'analyse et de recherche mis en place par ESPACE, au sein de l'Observatoire Hommes-Milieux « Littoral méditerranéen » (OHM-LM<fnref n="4" />, composante du LabEx DRIIHM<fnref n="5" />). Il présente le développement d'un questionnement portant sur les types et les formes d'espaces urbains qui s'étendent le long des rivages, les périodes et les lieux de leur apparition, leur évolution au cours du temps, les politiques publiques qui les encadrent, certains des facteurs économiques qui les sous-tendent, et les représentations qu'en ont les acteurs territoriaux comme les habitants. La recherche a débuté sur quatre sites géographiques de la côte méditerranéenne française, mais elle peut être transposée sur d'autres terrains. D'ores et déjà, l'article sert de référence aux travaux qui sont publiés dans le domaine, avec plus d'une dizaine de citations dans des supports listés dans le Web of Science. Trois ans après sa parution, il est en effet l'objet de 11 citations sur 11 supports différents. Certes, cette démarche n'épuise pas la totalité des actions de recherche possibles pour un décryptage complet du phénomène d'urbanisation des littoraux. Toutefois, un cadre d'observation et d'analyse solide s'est mis en place, conforté et exploité par les travaux conduits avec d'autres collègues au sein de l'OHM-LM.

Cette publication témoigne par ailleurs de la capacité d'ESPACE à élaborer et développer ses recherches avec d'autres laboratoires de sciences humaines et sociales comme de sciences de l'environnement, ici les laboratoires TELEMME<fnref n="6" /> (UMR 7503) et SPE<fnref n="7" /> (UMR 6134), et avec des acteurs territoriaux, ici l'Office de l'environnement de la Corse. Elle illustre également l'aspect transversal des recherches menées au sein d'ESPACE, puisque l'article associe des membres des trois sites d'implantation de l'unité (Aix-Marseille, Avignon, Nice), ainsi que la capacité à travailler en synergie, en associant des auteurs de statuts et compétences diversifiés (chercheur CNRS, enseignant-chercheur, ingénieur et post-doctorant).

Enfin, la recherche présentée témoigne clairement de la mise en relation des domaines de compétences inscrits dans le programme scientifique d'ESPACE. Elle s'insère à la fois dans l'axe « Complexité spatiale et trajectoires de territoires » (action « Systèmes territoriaux littoraux en Méditerranée : entre opportunités et contraintes »), dans l'axe « Méthodologies des spatialités » (actions « Référentiels de l'observation statistique et spatiale » et « Dynamiques de changement et espace »), et dans l'axe « Transition écologique, savoirs et innovation » (action « Transfert des connaissances territoriales complexes »). La publication démontre donc que l'articulation des questionnements apporte une plus-value certaine aux travaux mis en œuvre. D'autres publications, dont la liste est fournie avec le bilan du contrat scientifique, ainsi que l'obtention du financement de plusieurs projets de recherche lauréats de différents appels d'offre, en témoignent également.

<footnote n="4">⁴ https://www.driihm.fr/les-ohms/128-l-ohm-littoral-mediterranean</footnote>

<footnote n="5">⁵ https://www.driihm.fr/</footnote>

<footnote n="6">⁶ https://telemme.mmsh.fr/</footnote>

<footnote n="7">⁷ https://spe.universita.corsica/</footnote>
A framework to analyse urban sprawl in
the French Mediterranean coastal zone

Samuel Robert, Dennis Fox, Guilhem
Boulay, Antoine Grandclément,
Marie Garrido, Vanina Pasqualini,
Aurélie Prévost, Alexandra Schleyer-

Regional Environmental Change

ISSN 1436-3798

Volume 19

Number 2

Reg Environ Change (2019) 19:559-572

DOI 10.1007/s10113-018-1425-4

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ORIGINAL ARTICLE

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CrossMark

# A framework to analyse urban sprawl in the French Mediterranean coastal zone

Samuel Robert<sup>1,2</sup> · Dennis Fox<sup>1</sup> · Guilhem Boulay<sup>1</sup> · Antoine Grandclément<sup>3</sup> · Marie Garrido<sup>4,5</sup> · Vanina Pasqualini<sup>5,6</sup> · Aurélie Prévost<sup>1</sup> · Alexandra Schleyer-Lindenmann<sup>1</sup> · Marie-Laure Trémélo<sup>1</sup>

Received: 31 October 2016 / Accepted: 26 September 2018 / Published online: 9 October 2018
© Springer-Verlag GmbH Germany, part of Springer Nature 2018

## Abstract

As in many other European countries, urbanisation and urban sprawl along the French Mediterranean coast are a major concern. Understanding this phenomenon requires both multi-level and multi-disciplinary approaches. In this perspective, this article presents a framework for the observation and analysis of urban sprawl in the French Mediterranean coastal zone. Developed in the context of a scientific coastal observatory with four contrasting study sites, the framework was designed to structure the observation and analysis of urban sprawl dynamics and their driver variables. Although urban expansion is currently slowing in coastal zones, local exceptions can be found and accounted for by historical urban planning and environmental protection measures, local residential tax policies and contradictory perceptions of coastal zones by residents. Our multi-disciplinary initiative is capable of integrating different temporal and spatial scales and has proven relevant in analysing urban sprawl in coastal areas. It shows the need to study coastal areas at finer scales to identify specific dynamics in their local contexts, since these represent the scale at which administrative decisions are made.

**Keywords** Urban sprawl · Coastal zone · Mediterranean · Analytical framework

Editor: James D. Ford

<figure><img src="image_20.png" /></figure>

Samuel Robert
samuel.robert@univ-amu.fr

Dennis Fox
fox@unice.fr

Guilhem Boulay
guilhem.boulay@univ-avignon.fr

Antoine Grandclément
antoine.grandclement@univ-lr.fr

Marie Garrido
garrido@oec.fr

Vanina Pasqualini
pasquali@univ-corse.fr

Aurélie Prévost
aurelie.prevost@univ-amu.fr

Alexandra Schleyer-Lindenmann
alexandra.lindenmann@univ-amu.fr

Marie-Laure Trémélo
marie-laure.tremelo@univ-amu.fr

<footnote n="1">1. ESPACE, UMR 7300, CNRS, Aix Marseille Université, Avignon Université, Université Nice Sophia-Antipolis, F-06204 Nice Cedex, France</footnote>

<footnote n="2">2. ESPACE, UMR 7300, CNRS, Technopole de l'Environnement Arbois Méditerranée, Aix Marseille Université, BP 80-13545, Aix-en-Provence Cedex 04, France</footnote>

<footnote n="3">3. TELEMME, UMR 7303, CNRS, Aix Marseille Université, F-13094 Aix-en-Provence Cedex 2, France</footnote>

<footnote n="4">4. Department Strategies and Marine Sciences, Office of Environment of Corsica, F-20600 Bastia, France</footnote>

<footnote n="5">5. Sciences pour l'Environnement, UMR 6134, CNRS, Université de Corse, F-20250 Corte, France</footnote>

<footnote n="6">6. Stella Mare, UMS 3514, CNRS, Université de Corse, F-20250 Corte, France</footnote>
## Introduction

Increased pressure to develop sound land-use policies, protect biodiversity and anticipate coastal natural risks has made the rapid urbanisation of coastal areas a major environmental issue. Coastal zones came under pressure during the second half of the twentieth century as people migrated for both tourism and residential purposes and as maritime shipping increased in the global economy (Small and Nicholls 2003; Nicholls et al. 2007). This trend caused a number of environmental and socio-economic problems, particularly in the Mediterranean basin (Cori 1999; Datar 2004; Benoit and Comeau 2005; Gómez and Picarzo 2006; Mcdonald et al. 2008; Hadley 2009).

The Mediterranean coast of France, one of Europe's most urbanised coastal areas (EEA 2006), embodies many of the issues underlying urban sprawl. Agricultural areas and unprotected natural areas (offshore and inland) are threatened by urban expansion (Meinesz et al. 1991; Medail and Diadema 2006; Abrantes et al. 2010; Doxa et al. 2017), resulting in a loss of habitats and biodiversity and a standardised landscape quality. The strong land pressure also makes it very difficult for locals to obtain housing (DATAR 2004) and for businesses to set up close to the coast, except in the tourism and real estate sectors (Daviet and Robert 2012). In 2006, artificial land cover accounted for 31.6% of land located within 500 m of the sea (compared to a mean of 28.2% for French coastal zones in general) but reached 49.6% in southeastern France between Marseille and the Italian border (ONML 2009). Between 2008 and 2012, French coastal municipalities occupying only 4% of the territory accounted for 11.2% of new housing areas, and 40.4% of new housing areas in coastal municipalities were located on the Mediterranean coast (ONML 2014). This trend is likely to continue (Léon 2010) and raises several social, political and scientific issues. For example, how has coastal development been integrated into land-use planning since the landmark Brundtland report (1985) and especially Agenda 21 of the 1992 Rio summit (Cicin-Sain and Knecht 1998; Vallega 1999), whose principles were transposed to France (Deboudt et al. 2008)? Since a total freeze on coastal development is neither feasible nor acceptable, how can coastal benefits be maximised while protecting the environment? Given the exceptionally high tourism pressure on the Mediterranean basin coast (500 million tourists predicted in 2030 compared to 343 million in 2014, according to UNTWO<fnref n="1" />), can the French coast escape an urbanisation process that spares only a few privileged protected natural habitats? These questions are pushing researchers to identify current dynamics, analyse the underlying processes and orient public policy.

Coastal development has been studied for some time, and rapid urbanisation has attracted considerable attention as a dominant trend that is fundamentally non-sustainable if coastal areas are to be preserved. Studies have quantified the area lost to urbanisation, characterised urban morphologies, and at times modelled future trends according to different scenarios (Weng 2002; Catalàn et al. 2008; Petrov et al. 2009; Pons and Rullan 2014; Romano and Zullo 2014). In addition, some studies have focused on the factors driving urbanisation, including the impacts of land market and urban planning policies, which can orient coastal development (Razin 1998; Malvárez Garcia et al. 2003; Crawford 2007; Tang 2008; Abrantes et al. 2016). In recent years, coastal submersion and relocation strategies have begun to appear in the scientific literature (Duvat et al. 2013; Meur-Férec 2014; Poumadère et al. 2015; Rey-Valette et al. 2015). Yet despite the number of studies and fields of research, there is a lack of integrated multi-disciplinary studies of urban dynamics and their environmental impacts. In general, studies isolate one aspect of the issue, and obtaining a complete picture is difficult.

Our objective is to present a framework for the observation and analysis of urban sprawl on the French Mediterranean coast. Its design was based on a scientific coastal observatory with four study sites. Both the framework and its results are described below.

## Framework description

### The human-environment observatory for Mediterranean coasts

The 'Observatoire Hommes-Milieux Littoral Méditerranéen' (OHM-LM)<fnref n="2" /> was co-created by the Institute of Ecology and Environment (INEE) and the Institute of Social and Human Sciences (INSHS) of the French National Scientific Research Centre (CNRS)<fnref n="3" /> in 2012 with a mandate to study and compare human-environment interactions in Mediterranean coastal zones. Each year, it publishes a call for proposals to finance research projects on a wide range of topics and to encourage interactions among disciplines and with local land managers through seminars and other events (Chenorkian 2014). Since its creation, the OHM-LM has financed 36 studies, six of which focused specifically on coastal urbanisation; two additional studies on this issue had external funding but were conducted under the OHM-LM. The observatory fosters the development of multi-disciplinary research on urban sprawl and urbanisation integrating a range of themes like land use, land ownership, landscapes, urban planning and hydrology. Initially launched as individual projects, the studies tend to

<footnote n="1">¹ United Nations World Tourism Organisation press release, 15035, 27 May 2015</footnote>

<footnote n="2">² URL: http://www.ohm-littoral-mediterranean.fr/</footnote>

<footnote n="3">³ Centre national de la recherche scientifique. URL: http://www.cnrs.fr/</footnote>
converge over time as researchers meet to present results, harmonise their spatial and temporal scales of analysis and share data. This evolution has led to the ongoing development of a scientific framework for the study of urban sprawl in coastal areas.

## Study sites

The framework relies on studies conducted in the four OHM-LM observatory sites shown in Fig. 1. Two of the sites are located on the continent (Marseille coastal zone and Gulf of Aigues-Mortes) while the other two are situated on the island of Corsica (Coast of Balagne and Coast of Biguglia). These sites were selected to represent a gradient from low to high urban pressure contexts (Table 1). Balagne is the least urbanised while Marseille is the third largest urban area in France, with major industrial and port infrastructures. However, the two Corsican study sites have recently recorded a strong population increase, unlike the two continental sites. In all, 50 municipalities are included within the observatory perimeter and represent a wide range of environmental and socio-economic conditions: low-lying lagoon coasts, rugged coastlines, coastal risks (erosion, marine submersion), different categories of protected areas, seaside resorts, large and small towns, industrial and yachting harbours, more or less seasonal tourism. This enables a wide range of topics and contexts to be taken into account.

Depending on research objectives, the actual geographical area under study may differ from one project to another. Coastal zone limits for the study sites depend on municipal administrative limits (1986 coastal law), which explains why some municipalities do not extend to the sea but have shores along a lagoon. To address the actual geographical area in question, some studies focus on smaller areas while others extend further inland.

## Research themes

Four main research themes form the core of the OHM-LM framework (Fig. 2).

### Land use/cover change (A)

The study of land use/cover change (LUCC) examines how urban areas have evolved over space and time. Based on aerial photographs and geographical databases, large-scale land-use maps are created in a Geographical Information System (GIS) environment as we already explained in Robert et al. (2015) and Robert (2016). In addition to describing changes in land cover and urban morphology, particular attention is paid to land cover transition dynamics between categories. Both urban sprawl and renewal are quantified and discrepancies in rates between major urban areas are analysed. National

('Institut Géographique National'—IGN) and local databases are merged with data acquired from digitising high-resolution aerial photographs to create thematic maps and characterise urban structures and changes at different spatial and temporal scales.

### Spatial planning (B)

Coastal LUCC dynamics and urban sprawl can only be understood in the light of historical land-planning documents and how elected representatives have exploited these tools or not since the 1967 land planning law, a milestone in the French spatial planning system. For each municipality, a database that includes all past zoning documents and environmental regulations was created, as presented in Prévost and Robert (2016). The database has two major entries: (1) environmental protection measures with the areas concerned and corresponding urban constraints, and (2) all zoning documents approved at municipal and supra-municipal levels since 1967. These data are then run through statistical and multi-criteria analyses to define territorial urban planning anticipation and land-use management typologies. These can then be used to generate maps at municipal level in order to identify local trends and anomalies.

### Local tax policies (C)

In addition to urban planning strategies, fiscal policies also play an important role in orienting urban sprawl and LUCC as we demonstrated in Grandclément and Boulay (2016). The research objective is to investigate how tax income from new residents can be a driver or obstacle to urban sprawl. Generating income from local taxes in a context of diminishing state funding transfers can be perceived as attractive and even essential for some municipalities. Data from the public finance department ('Direction générale des finances publiques'—DGFIP) were used to calculate the proportion of municipal funding from residential taxes,<fnref n="4" /> and this was then used to characterise municipal resources. Identifying municipalities with a strong income dependence on residential taxes is a first step in investigating how this impacts urbanisation and determining which municipalities maintain or lose potential for future growth. The different scenarios may help predict trends for the near future. The study area goes beyond the limits of the OHM-LM observatory sites by including the entire French continental Mediterranean coast (60 km inland, 1450 municipalities). Corsica is not included because of unavailability of data. The study covers the Gulf of Aigues-Mortes and coast of Marseille (34 municipalities in all).

<footnote n="4">⁴ 'Residential taxes' mean taxes paid by households to their municipality of residence. They refer to both the housing tax and the property tax on buildings.</footnote>
<figure><img src="image_21.png" /><figcaption>Fig. 1 OHM-LM observatory study sites</figcaption></figure>

## Stakeholders' representations (D)

A wide range of social groups have direct and indirect interests in LUCC dynamics, either because they support urbanisation or because they wish to slow/stop urban sprawl. Such groups include residents, tourists, elected representatives, municipal employees and businesses. The research objective is to identify their perceptions of urbanisation in the coastal zone by bringing to light competing visions of socio-economic and environmental priorities and relating them to actual LUCC trends. This work was carried out mainly by sociologists, psychologists and geographers. One study<fnref n="5" /> in particular brought out the inherent ambiguities of perception by different interest groups (Robert et al. 2016). Through an online questionnaire (developed with LimeSurvey), we targeted all residents living in a Mediterranean coastal municipality between the Italian border and the Spanish border. In addition to basic identification questions (age, sex, profession, municipality of residence, number of years in municipality, type of residence), the

questionnaire was designed to elicit residents' perception of the coastal landscape. Dominant themes and associations between themes were identified by asking participants to build and give a title to two lists of words that they established from a pre-established list of terms evoking different aspects of the landscape (physical, biological, human impacts) and its perception (uses, emotional responses). A quantitative analysis (frequency, similarity, multivariate and automated content analyses using IRAMUTEQ) was run on the data collected to highlight the strongest associations within the lists and to build graphs representing the social representations of the French Mediterranean coastal zone (methodology developed by Degenne and Vergès (1973)). Between March and July 2014, 922 valid responses were recorded.

## Results

At this early stage in the OHM-LM history, a full systematic comparison of all themes and sites is not possible, so selected examples are considered below. In some cases, studies were limited to the two sites most relevant to research objectives. As expected, sites do not display a single homogeneous

<footnote n="5">⁵ VIPLI-Med (Intégrer une connaissance de la représentation et de la visibilité et des représentations du paysage pour la gestion et la planification de l'espace en zone côtière. Analyse spatiale et approche socio-culturelle sur trois littoraux méditerranéens): project funded by Fondation de France.</footnote>
<table><caption>**Table 1** Main characteristics of the four study sites</caption><thead><tr><th>Study area</th><th>Coast of Balagne</th><th>Coast of Biguglia</th><th>Gulf of Aigues-Mortes</th><th>Marseille coastal area</th></tr></thead><tbody><tr><td>Number of municipalities</td><td>12</td><td>4</td><td>14</td><td>20</td></tr><tr><td>Area (km²)</td><td>482</td><td>123</td><td>438</td><td>965</td></tr><tr><td>Population 2012</td><td>17,563</td><td>25,971</td><td>121,939</td><td>1,196,852</td></tr><tr><td>Population density 2012 (people/sq. km)</td><td>36</td><td>211</td><td>278</td><td>1241</td></tr><tr><td>Mean annual population growth rate 1999–2012 (%)</td><td>1.53</td><td>2.99</td><td>1.45</td><td>0.49</td></tr><tr><td>Artificial area (CLC 2012) %</td><td>2.9</td><td>17.0</td><td>13.3</td><td>35.8</td></tr><tr><td>Main characteristics of the coastal zone</td><td>Rocky shores. Small old cities on the seashore. Recent coastal resorts. Villages on the hilltops. First tourist destination in Corsica</td><td>Protected coastal lagoon. Inland urban development close to Bastia (major economic pole in northern Corsica)</td><td>Vast complex of several lagoons. Strong coastal erosion of the barrier beach. Small cities, large coastal resorts and yachting harbours on the barrier beach. Close to Montpellier (popn. about 500,000)</td><td>Large coastal plain (west), lagoon (north), rocky shores and hilly topography (south and east). Largest industrial harbour in France. Third largest city of France. Calanques national park and other protected areas on the coast nearby</td></tr></tbody></table>

Data: INSEE 2015; EEA 2014

dynamic, but reflect site heterogeneities that arise from the local context. However, all sites provide evidence that urban sprawl is the dominant model of local development, contradicting principles of integrated management and balanced development.

## Land use/cover changes

In terms of LUCC dynamics, two of the four study sites (four Marseille site municipalities located in what is known as the ‘Côte Bleue’ and four from the Biguglia coast) show opposing

trends in urban sprawl patterns over recent years (Robert et al. 2015). The two sub-zones of the study sites have similar populations (about 25,000) and are located near major urban centres dominating urban change dynamics. However, urban growth in 2002–2011 in the Biguglia sub-zone was more than nine times greater than that in the ‘Côte Bleue’ Marseille site sub-zone (Table 2). Urban renewal ratios vary substantially both within and between sites. While urban renewal was well established in the municipalities of the ‘Côte Bleue’, it remains weak in Biguglia. The maximum ratio between artificial area annual growth rates is 4.7 in Biguglia (Furiani versus

<figure><img src="image_22.png" /><figcaption>**Fig. 2** Urban sprawl assessment framework at a local level</figcaption></figure>
<table><caption>**Table 2** Urban LUCC dynamics</caption><thead><tr><th>Site</th><th>Commune</th><th>Artificial areas 2011 (%)</th><th>Mean annual evolution (%)*</th><th>Urban sprawl (ha)*</th><th>Urban renewal (ha)*</th><th>Ratio urban renewal/ urban sprawl*</th></tr></thead><tbody><tr><td rowspan="5">'Côte Bleue'</td><td>Carry-le-R.</td><td>16.24</td><td>0.33</td><td>33.4</td><td>52.4</td><td>1.6</td></tr><tr><td>Ensuès La R.</td><td>30.62</td><td>0.08</td><td>1.9</td><td>11.2</td><td>5.9</td></tr><tr><td>Le Rove</td><td>12.69</td><td>0.39</td><td>10.5</td><td>22.0</td><td>2.1</td></tr><tr><td>Sausset-les-P.</td><td>10.17</td><td>0.60</td><td>13.0</td><td>9.3</td><td>0.7</td></tr><tr><td>Biguglia</td><td>23.61</td><td>0.33</td><td>8.0</td><td>9.9</td><td>1.2</td></tr><tr><td rowspan="5">Biguglia area</td><td>Carry-le-R.</td><td>19.14</td><td>3.04</td><td>587.0</td><td>136.5</td><td>0.2</td></tr><tr><td>Ensuès La R.</td><td>23.29</td><td>2.60</td><td>132.0</td><td>26.2</td><td>0.2</td></tr><tr><td>Le Rove</td><td>15.81</td><td>3.97</td><td>229.7</td><td>53.0</td><td>0.2</td></tr><tr><td>Sausset-les-P.</td><td>18.14</td><td>0.85</td><td>31.8</td><td>18.3</td><td>0.6</td></tr><tr><td>Biguglia</td><td>21.50</td><td>3.78</td><td>193.5</td><td>39.0</td><td>0.2</td></tr></tbody></table>

Borgo) and more than 7.5 in the 'Côte Bleue' (Le Rove versus Carry-le-Rouet). This demonstrates that although the framework for planning laws is national, local priorities and dynamics dominate trends.

In Biguglia, the urban footprint shows greater growth and urban expansion is more dispersed spatially, with new artificial surfaces popping up in discrete patterns (Fig. 3). This violates the dominant national-level governing principle of allowing growth to expand from current urban areas. Several new urbanised sites created in 2002–2011 are isolated from pre-existing urban space, with no apparent spatial strategy for development. In the 'Côte Bleue' municipalities, contrastingly, new urban spaces are relatively rare and generally established in continuity with existing urban zones.

Urban renewal in both sites led to increased urban density and new residential developed areas, especially in the 'Côte Bleue' zone as we demonstrated in Robert (2016). Individual housing outnumbers collective housing, evidence of residents' preferences as favoured by zoning laws. For Biguglia, however, urban renewal also generated new developed areas for economic activities serving the logistics needs of the zone close to Bastia (the main point of entry to Corsica).

## Spatial planning and environmental protection measures

One of the explanations for these differences between the two sites ('Land use/cover changes' section) lies in their zoning documents and environmental protection measures. We were able to make a full comparison of the four study sites since documentation was collected and analysed for the 50 municipalities concerned. The analysis shows that despite an extensive legal framework empowering municipalities to act on ecological issues, its use by elected representatives varies from site to site, regardless of the biogeographic or ecological pertinence of local conditions. The Gulf of Aigues-Mortes is

among the most active in promoting environmental protection, both in terms of the number of decisions and in terms of the ensuing constraints (Fig. 4). In Balagne, six municipalities are poorly protected while protection in Biguglia is intermediate. The coastal zone of Marseille is quite well-protected, but protection is weaker around the Berre lagoon, a large industrial zone, than in the 'Côte Bleue' and bay of Marseille areas.

Urban planning policies reflect the discrepancies in environmental protection described above, which we highlighted in Prévost and Robert (2016). At the inter-municipal level, analysis shows that municipalities vary widely in their capacity to anticipate urban pressure by implementing planning documents according to national legal frameworks (Fig. 5). Only the four municipalities of the 'Côte Bleue' showed an early and consistent will to anticipate change and control urban sprawl in conformity with the national framework and local priorities, which may explain their limited urban expansion in recent years. Other municipalities hesitated to implement inter-municipal land planning. At best, planning occurred within the 'SCOT' framework (French territorial-level planning guidelines), but no other inter-municipal planning documents were produced before 2000 (Gulf of Aigues-Mortes and half the remaining municipalities of the coast of Marseille). While the west of the coast of Marseille and Balagne have their 'SCOT' under study but not approved, urban planning is at its worst at the Biguglia site, where inter-municipal planning documents have not even been prepared. This indicates that the management of urban sprawl is worst at the Biguglia site, which is failing to meet national-level priorities and where growth is most sporadic.

## Local tax policies

The income tax study monitors changes in the share of municipal incomes derived from residential taxes (which we call

<footnote>*Period 2002–2011 for the Biguglia site and 2003–2011 for the Blue Coast site</footnote>
<figure><img src="image_23.png" /><figcaption>**Fig. 3** Urban sprawl on the Blue coast and the Biguglia lagoon area</figcaption></figure>

‘residentialisation’) and the fiscal levers pulled by municipalities. Our results highlight that income from residential taxes is greater than the French national average, 63% versus 58%

respectively (Grandclément and Boulay 2016). Between 2002–2003 and 2009–2010, the share of income derived from residential taxes increased but at a rate slower than the national
<figure><img src="image_24.png" /><figcaption>**Fig. 4** Environmental protection levels</figcaption></figure>

average, +3.3% versus +4.2%. A finer analysis of the two continental observatory sites reveals some interesting details.

Within the Gulf of Aigues-Mortes site, the proportion of residential taxes evolved differently: in half the municipalities, it decreased, while in seven others, it increased. Increases tend

to be concentrated in the eastern sector of the study site, in municipalities farther from the major urban centres of Montpellier and Nîmes (Fig. 6). Maps in Fig. 6 also show a decrease in the proportion of municipal income from residential taxes in municipalities bordering the coast. In the coast of
<figure><img src="image_25.png" /><figcaption>**Fig. 5** Planning practices at the inter-municipal level</figcaption></figure>

Marseille zone, the evolution is more homogeneous. In 12 out of 20 municipalities, there is a moderate increase, while in the industrial zone west of the Berre lagoon, three municipalities show strong increases and three others slight decreases. High-tourism municipalities show a clear negative trend in the

proportion of income attributable to residential taxes. In the municipalities of Sausset-les-Pins and Carry-le-Rouet in the ‘Côte Bleue’ zone, evolution is coherent with urban planning, showing that residential tax income is not encouraging municipalities to expand their urbanised areas.
<figure><img src="image_26.png" /><figcaption>**Fig. 6** Evolution in the share of residential taxes in municipal incomes</figcaption></figure>

Municipalities can increase residential tax income either by expanding fiscal bases (new residences) or by raising tax rates. Growth of the proportion of residential taxes has slowed in most municipalities (Fig. 7), so it appears that they no longer wish to, or cannot, use new urbanisation or higher tax rates to increase their incomes. Only three municipalities clearly diverge from this trend: Villeneuve-lès-Maguelonne, Marsillargues and Ensuès-la-Redonne. Here, both fiscal bases—contributing surface area and tax rate—have increased. Others, near the Berre lagoon and the Gulf of Fos-sur-Mer, show more moderate growth patterns. In summary, tax strategies are constrained by urban structure, since coastal municipalities do not have substantial leeway to develop new urban areas due to a lack of available space.

## Stakeholders' representations

Local populations perceive land cover changes in the coastal zone in various ways, and our study brings out some of the ambiguities in this perception (Robert et al. 2016). Associations identified by at least 20% of the participants revealed two distinct poles in Fig. 8. The first corresponds to positive associations with the coastal zone: centred on the term ‘coastal footpath’ (‘sentier côtier’), it reveals associations with the natural landscape, recreational uses and pleasant feelings. This pole is relatively well-structured, with terms related in numerous word triangles, but the strongest association is between ‘coastal footpath’ and scrubland (‘garrigue’), indicating that land elements feature more strongly in perceptions than
<figure><img src="image_27.png" /><figcaption>**Fig. 7** Residentialisation of municipal income</figcaption></figure>

marine elements. The second pole, made up of fewer terms, appears to stand in opposition to the first, associating negative aspects of urbanisation such as ‘building’ and ‘transformation’. This pole is highly structured, therefore strongly representative of the social perception of the coast.

The coastal zone appears to generate contradictory feelings in local residents. On the one hand, they appreciate it as a natural environment and recreational area, but on the other they feel that land cover changes, urbanisation in particular, are deteriorating it. Despite this, the residents idealise the

<figure><img src="image_28.png" /><figcaption>**Fig. 8** Characteristics of the coastal landscape showing word associations between at least 20% of respondents</figcaption></figure>
environment and consider themselves fortunate to live and holiday there. This tends to legitimise urbanisation and may explain our observations in the ‘Côte bleue’ and Biguglia sites (‘Land use/cover changes’ section), where both urban expansion and renewal have mainly produced individual houses in recent years, reinforcing the residential function of these territories.

## Discussion and perspectives

### The framework as a key to understanding urban sprawl in coastal zones

Although national-level data show that coastal development is slowing (ONML 2014), more detailed studies of our sites indicate that some municipalities diverge substantially from this trend. While the coast of Marseille follows national trends, the area around Biguglia, for example, is undergoing rapid urban expansion. These differences are related to how long ago urbanisation began and to a general metropolisation. As elsewhere in France, municipalities located farther from big cities are growing most rapidly. However, urban development is not necessarily compromised in regions where urban expansion is limited, because urban renewal and densification allow municipalities to continue to grow socially and economically. Both urban planning (and anticipation) and residential tax income policies are important factors explaining trends in urban development. For example, municipalities pursuing active environmental protection and strong urban planning policies have mastered urban expansion best. Municipalities that have done neither show strong, sometimes negligent, urban expansion. These trends can also be related to municipal fiscal policies. Further studies are needed, but our results suggest several hypotheses. Municipalities where residential tax contributions have increased as a proportion of total municipal income are probably municipalities where urban planning has been limited or largely absent. Municipalities showing opposite trends tend to have a long history of urban planning that is continuously updated. Yet regardless of coastal urban dynamics, the results on social perceptions of coastal zones show contradictory trends. Coastal areas are perceived as natural environments well suited to relaxation and recreational activities, and urban development as a negative encroachment on the landscape. However, development is needed to enable and maintain recreational activities, so it is difficult to have one without the other. Urban expansion in the coastal zone is therefore at the heart of coastal management in general and the conflicting positions of conservation and development. This, in turn, indicates that coastal management must be dealt with at different territorial scales, in particular beyond municipal limits, i.e., by reconsidering the perimeter for management of coastal resources.

### A framework that needs to be consolidated

While our results are promising, they point to the need to expand the field of study: the explanatory framework remains incomplete. Useful additional factors include geographical (distance to urban centres, service provider locations, available land area for development), socio-political (general principles, local cultural practices, local political priorities) and, of course, a range of economic factors. In addition, past and ongoing studies need to be expanded to include all four OHM-LM observatory sites and even, on some issues, covering the entire French Mediterranean coast. This would enable a fuller comparison of results from the different contexts. Urban planning and fiscal strategies could be treated more systematically at the municipal administrative scale. Integrating recent and developing databases would help extend analyses to new directions, fostering a better understanding of the urbanisation process as a whole. Finally, longer time scales need to be considered. Most studies have looked at relatively recent changes (last 50 years for urban planning, 20 years for land cover mapping and 10 years for fiscal policies). But socio-economic dynamics evolve over longer periods, so many observatory studies should consider longer time scales (at least from the mid-nineteenth century, taking into account both the effect of the industrial revolution and rising coastal tourism), as recently done for the coast of Balagne (Cancelieri and Maupertuis 2016). Modelling needs to be reinforced to include predictive studies of future urban expansion, as was done for the Marseille coast site (Voiron et al. 2014). These changes must be carried out in collaboration with local land managers to enhance our collective understanding of LUCC dynamics and better identify strategic options for the future in a decision-support perspective.

### Widening the scope

Four new directions have been identified to widen the scope of future research priorities in the OHM-LM observatory sites. The first concerns the potential for maintaining agricultural practices within the coastal zone to promote a balance between land uses, landscape types and ecological entities. This research would require the involvement of a wide range of disciplines (especially ecology, agronomy, geography, sociology and economy). The second new research direction concerns environmental risks created by urban expansion along coasts, seeking to improve risk management strategies and land-use planning. Both coastal submersion and erosion are important, but other risks, such as flooding, storm runoff accumulation, mass movements and forest fires, must also be included depending on local circumstances. In a context of climate change, whether urban dynamics are oriented towards expansion or densification, there are major implications for risk exposure and social vulnerability. Recent tragic events
have demonstrated this: catastrophic 2015 flooding on the Côte d'Azur, 2014 and 2015 flooding in Corsica, 2014 flooding in the Hyères Bay area and devastating fires in the 'Côte Bleue' in 2009 and 2016, in Corsica in 2014, and along the Var coast in 2003. The third research priority is oriented towards maintaining ecological continuity within coastal cities and between the coast and inland areas. Urban dynamics in coastal environments influence habitat quality and morphology, environmental priorities and the place of living species in developed environments. An integrated approach linking urban, agricultural and natural coastal environments could be based on priorities defined in the above two research areas. The fourth and final research axis should deal with whether socio-demographic dimensions of urbanisation are consistent with the model of sustainable development. The numerous amenities of coastal zones make them particularly attractive (and therefore expensive), so they are often occupied by wealthier social classes, and access is often more limited for the less wealthy. This corresponds to the concept of ecological inequality, and coastal development should evolve to integrate more equal access to landscapes, beaches and the coast in general.

## Conclusion

Although France remains a country with a strong central government, and even though its territorial management policies are generally considered efficient, coastal development along the Mediterranean shows strong regional contrasts. Despite a tendency towards slower urban expansion in coastal zones, local exceptions can be found and accounted for by historical urban planning and environmental protection measures, local residential tax policies and contradictory perceptions of coastal zones by residents. These factors contribute to the different urban trajectories encountered in the study sites. The multidisciplinary framework capable of integrating different temporal and spatial scales described here has proven useful for analysing urban sprawl and LUCC dynamics in coastal areas. It also shows the need to study coastal areas at a fine scale in order to identify specific dynamics in their local context, since this is the scale at which administrative decisions are made. Despite repeated calls in recent years to change our relationship with the environment, to adapt to climate change, to reduce our ecological footprint and to develop integrated management strategies, it is clear that coastal management frequently fails to meet these expectations. Local scale studies are therefore necessary to measure how local interests and priorities interact with national-scale policies. Scientists must collaborate more closely with local land managers and residents in order to develop a shared understanding of dynamics and to envisage new pathways for future development.

**Funding information** This work was funded by the Labex DRIHM, French programme "Investissements d'Avenir" (ANR-11-LABX-0010) which is managed by the ANR.

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#3 - ARTICLE

FROM THE STREET TO THE METROPOLITAN REGION: PEDESTRIAN PERSPECTIVE IN URBAN FABRIC ANALYSIS

Araldi A. (Post-doc, ESPACE), Fusco G. (DR, CNRS)

2019, *Environment and Planning B: Urban Analytics and City Science*, Volume 46(7), pages 1243-1263
https://doi.org/10.1177/2399808319832612

https://hal.archives-ouvertes.fr/hal-02557278v1

Cette publication constitue un jalon fondamental pour les recherches d'ESPACE en matière d'« urban morphometrics », c'est-à-dire des méthodes issues de l'analyse spatiale pour l'étude de la forme urbaine et de ses transformations plus récentes. Elle participe à la fois à l'action « Complexité urbaine et réseaux » de l'axe 1 et à l'action « Données géographiques massives, modélisation et IA » de l'axe 2. Elle témoigne également de la capacité du laboratoire à former et faire émerger des jeunes chercheurs, son premier auteur ayant été doctorant puis post-doctorant à ESPACE.

L'article, publié sur EPB<fnref n="8" />, une des revues de référence en géographie urbaine quantitative, présente un protocole novateur d'urban morphometrics, le *Multiple Fabric Assessment (MFA)*. Le protocole permet l'identification et la caractérisation data-driven de différents types de tissus urbains à l'échelle d'une aire métropolitaine. Dans ce programme sexennal, le MFA a permis une forte accélération des recherches sur la complexité de la forme urbaine des métropoles contemporaines, l'établissement de nombreuses collaborations de recherche à l'international (Univ. Catholique de Louvain<fnref n="9" />, Univ. de Tokyo<fnref n="10" />, Univ. Dokuz Eylül Izmir<fnref n="11" />, Chalmers Univ. Göteborg<fnref n="12" />, Univ. of Strathclyde Glasgow<fnref n="13" />) et la création d'un partenariat stratégique avec un industriel, la start-up sophipolitaine Kinaxia<fnref n="14" />, d'abord dans le cadre du projet UrbaSpace de l'IDEX UCA JEDI (2019-2021), ensuite par l'obtention de l'ANR LabCom K-City-E-Space (2021-2026).

Le tissu urbain est la forme typique de relations spatiales existant entre les bâtiments, les rues, les parcelles et le site à l'échelle la plus fine de l'analyse morphologique (celle de l'îlot, de la rue ou du fragment urbain). Notre premier objectif a été de développer une méthode d'identification et d'analyse par géo-traitement des tissus urbains d'un vaste espace métropolitain intégrant le point de vue du piéton qui se déplace dans la ville. Il devient alors possible d'appréhender la diversité des formes urbaines des grandes métropoles, de parvenir à la délimitation de leurs régions morphologiques, d'en étudier l'organisation spatiale et d'évaluer l'apport de la métropolisation au développement de nouvelles formes. Le MFA s'articule en trois phases :

1. Le calcul d'indicateurs morphométriques, permettant de décrire les caractéristiques du tissu urbain observables par un piéton qui se déplace dans l'espace public ;

2. Le développement d'une nouvelle procédure géostatistique de clustering spatial sur réseau pour identifier des structures spatiales significatives ;

3. Le clustering à base d'IA bayésienne des structures spatiales ainsi trouvées pour identifier et caractériser des tissus urbains sur l'ensemble des indicateurs retenus.

Grâce à son approche vectorielle (parcellaire, bâti et réseau viaire), le MFA évite l'appauvrissement de l'appréhension de la forme urbaine des démarches à base de maillages. Un schéma archétypique de l'organisation spatiale a ainsi pu être établi, montrant des régularités dans les relations entre régions morphologiques des principales villes azuréennes.

<footnote n="8">⁸ https://journals.sagepub.com/home/epb</footnote>

<footnote n="9">⁹ https://uclouvain.be/fr/index.html</footnote>

<footnote n="10">¹⁰ https://www.u-tokyo.ac.jp/en/</footnote>

<footnote n="11">¹¹ https://www.deu.edu.tr/</footnote>

<footnote n="12">¹² https://www.chalmers.se/en/Pages/default.aspx</footnote>

<footnote n="13">¹³ https://www.strath.ac.uk/</footnote>

<footnote n="14">¹⁴ https://www.kinaxia.fr</footnote>
<figure><img src="image_29.png" /></figure>

Special issue article

# From the street to the metropolitan region: Pedestrian perspective in urban fabric analysis

Alessandro Araldi and Giovanni Fusco

Université Côte d'Azur, CNRS, ESPACE, France

EPB: Urban Analytics and City Science

2019, Vol. 46(7) 1243–1263

© The Author(s) 2019

Article reuse guidelines:

sagepub.com/journals-permissions

DOI: 10.1177/2399808319832612

journals.sagepub.com/home/epb

<figure><img src="image_30.png" /></figure>

SAGE

## Abstract

The urban fabric is a fundamental small-scale component of urban form. Its quantitative analysis has so far been limited either in its geographical extent or in the diversity of components analysed. Moreover, the planning approach has traditionally privileged an aerial perspective. A new approach integrating the pedestrian point of view is proposed. Spatial analysis procedures are implemented with a twofold objective: identifying urban fabrics and studying their spatial organization within a large metropolitan area. The former is achieved through multiple fabric assessment, a three-step protocol using a network-based partition of urban space: (i) a set of skeletal streetscape indicators is implemented on each spatial unit, considering different constituents of the urban fabric; (ii) spatial patterns on the street network are identified, applying geostatistical analysis to each indicator; and (iii) spatial patterns with Bayesian clustering are recombined, allowing the identification and characterization of urban fabric types and subspaces within the city. This methodology is tested on the French Riviera metropolitan area, where nine families of urban fabric are identified. Disentangling the spatial organization of urban fabrics represents the second objective of this paper: the geographical distribution of urban fabrics is investigated, applying mathematical morphology and variography while considering network-constrained topological contiguities.

## Keywords

Urban form, urban fabric, network constrained local indicators of spatial association, Bayesian clustering, multiple fabric assessment

## Corresponding author:

Alessandro Araldi, Université Côte d'Azur, CNRS, ESPACE, Nice, France.

Email: alessandro.araldi@univ-cotedazur.fr
Introduction

Since Jacobs (1961) defended the idea that modern planning did not take into account the needs of city-dwellers, academics and practitioners have increasingly worked on the importance of pedestrian perspectives in urban studies, policies and planning. This is also true in the study of the physical form of the city, as pioneered by Lynch (1960). The pedestrian point of view is thus integrated into the analysis of urban spaces, focusing more specifically on the study of streetscapes. Both physical and qualitative aspects have been studied by urban designers (Ewing and Clemente, 2013) and computer-aided techniques are enriching the description of the physical streetscape. Nevertheless, streetscapes are not the only feature of urban form, and their morphological characteristics need to be put into the urban context where they are measured.

Urban morphologists proposed to study urban contexts through the analysis of the urban fabric, which is defined as a specific combination of local patterns made up of buildings, streets and plots and considered as the original and unique outcome of conscious and unconscious design of the physical city (Caniggia and Maffei, 1979). In turn, different types of urban fabric must be understood in the context of morphological regions (Conzen, 1960), revealing the multi-scale structuring of urban form. Thanks to the ever-growing availability of data and powerful geoprocessing tools, new methodologies have been developed to analyse the urban fabric by quantitative approaches. Specific goals underlie each procedure and result in different methodological choices. However, they barely consider the pedestrian point of view. In contrast, configurational approaches like Space Syntax (SSx) capture the pedestrian perspective, but do not focus on the urban fabric as defined by urban morphologists. The potential and constraints introduced by the pedestrian perspective of the urban fabric require specific methodological and conceptual considerations. These have been overlooked in traditional and more recent procedures.

Innovative approaches from other disciplines could bring several solutions that would allow traditional urban fabric analysis to fully integrate the pedestrian perspective. In particular, quantitative urban geography is well positioned to study urban form and design (Talen, 2011). Moreover, geographers have already adapted several geostatistical approaches to network-constrained space, being aware of its importance for the study of human activity in the city (Okabe and Sugihara, 2012).

The main goal of our paper is to present a new computer-aided methodology for the study of urban fabric, which is capable of integrating the pedestrian point of view. Geoprocessing, geostatistics and artificial intelligence (AI)-based clustering are implemented in a new procedure to analyse and combine ‘geometry and geography’ (Batty, 2007): from the geostatistical detection of urban fabric types to the study of their spatial organization within the urban space. A real-world case study of the size of an entire metropolitan area is used to test the method and evaluate the quality of the results obtained.

The paper is organized as follows. The next section provides an overview of computer-assisted methods available to analyse urban form, highlighting issues related to the pedestrian point of view. ‘Case study’ section introduces the study area of the French Riviera. ‘Urban fabric identification: A new methodology’ section describes multiple fabric assessment (MFA), a new three-step procedure for the identification of different urban fabrics. ‘Results: Identification of the urban fabric families’ section presents MFA results for the area studied. ‘Spatial organization of urban fabrics’ section shows how further geographical approaches might be applied to MFA outcomes to analyse the spatial organization of urban fabrics and to identify settlement boundaries. A discussion pertaining to future developments concludes the paper.
# Computer-aided approaches to urban morphology

## *The classical analysis of urban fabric*

Defined as the study of the urban form, urban morphology deals with the analysis of the physical layout and design of a city, a town or an urban fragment within these. Urban morphology involves the study of patterns of its components and the processes of their formation and transformation (Vernez-Moudon, 1997).

Literature on the analysis of urban form has its origins in the seminal works of urban geographers of the early 20th century, in German-speaking countries (Hofmeister, 2004). The works of the Conzenian and Muratorian schools after Second World War jointly developed the concept of the urban fabric. This is defined as a typical pattern of buildings, parcels and streets, characterizing the form of the physical city in a given historical and cultural context, at the scale of an urban fragment (Conzen, 1960; Muratori, 1959). The concept of the morphological region is also proposed to describe urban sectors characterized by consistent extents of a single urban fabric and to produce more global schemes of the morphological organization of the city. Despite efforts to develop systematic approaches for the analysis of the urban fabric, these methodologies relied heavily on manual measurements, treatment and interpretation of calculus. This has limited the extent of case studies (Allain, 2004; Borie and Denieul, 1984; Caniggia and Maffei, 1979). Morphological regions could be identified in small cities like Alnwick, Northumberland (Conzen, 1960), or in medium ones like Versailles (Castex et al., 1980), but have only been hinted at for big cities. Furthermore, the approach of classical urban morphologists to study the urban fabric left little space for the pedestrian perception of the physical city. Meanwhile, Lynch's (1960) seminal work established little link between the perception of cityscapes and the analysis of the urban fabric.

## *The configurational approach*

Starting from the late 1970s, technologies allowed the implementation of computer-aided quantitative approaches for the analysis of urban form (Kruger, 1977; Steadman, 1983). Graph analysis played an important role in these early models. Paralleling the development of social network analysis (Freeman, 1977), configurational approaches to urban street networks were proposed. SSx (Hillier, 1996; Hillier and Hanson, 1984) proposed the most innovative reconceptualization of urban form analysis by focusing on the open space available for pedestrians and by building on the theory of natural movement within this (Hillier et al., 1993). The urban open space is partitioned into convex polygons approximated with visual axial lines. Topological measurements of centrality are carried out on the resulting graph, which is a dual representation of the axial line network. A similar approach is the multiple centrality assessment (MCA, Porta et al., 2006; Sevtsuk and Mekonnen, 2012), which develops centrality measurements on the primal representation of metric street networks. Links represent street centrelines while nodes correspond to their junctions. Hybrid approaches have also been proposed between SSx and MCA (Cutini et al., 2005; Turner, 2007).

The use of visual lines and the natural movement theory might be considered as an integration of the pedestrian perspective applied to the analysis of urban form, and in SSx this is even truer than for MCA. But the final goal of configurational approaches is to study the organization of public space, open to pedestrian through-movement and to-movement. Such methods open the way to a deeper understanding of the relationship
between urban form and pedestrian movement. Seeing them as a computer-aided implementation of the analysis of the urban fabric, as suggested by Lévy (2005), is thus questionable. Important components of the urban fabric (buildings, plots, site), and their dimensional and geometrical relationships, lie outside of the scope of configurational analyses. Work is presently being carried out to combine urban fabric and configurational metrics in the description of urban form (Gil et al., 2012; Ye and Van Nes, 2014). However, Kropf (2017) highlights the need to reconceptualize urban fabric analysis when combining these different approaches. Our paper goes in this direction, leaving the integration with a configurative description of urban form as a future endeavour.

## Computer-aided analysis of the urban fabric

One of the main motivations of our paper is the development of new computer-aided methods to analyse both the form of the different urban fabrics, as defined by classical urban morphologists, and their organization in morphological regions within the city. Thus, we will turn our attention to the different methods that have been developed in this direction during the last decades. We propose to renew these by integrating the street-based pedestrian point of view. This will allow us to bridge the gap with works on streetscape perceptive analysis and to open the way for future convergence with other street-based analyses, like configurational approaches.

Following the founding concept of urban fabric, the reviewed methods aim to describe in different ways the arrangements of buildings, plots and streets. While configurational studies built their strength around a small number of shared methodologies, within this second group more goal-oriented procedures have been developed. All the methods considered integrate geoprocessing approaches to analyse the multidimensional character of the urban fabric. Different degrees of computer-aided automation allow the study of vast settlements at the fine grain scale of the single urban constituent (street, building, plot). The urban block represents the spatial unit of urban morphologists' traditional analysis: its historical permanence allows an easier diachronic analysis of built-up forms included in its limits. It is thus privileged in many geoprocessing methods as in Berghauser-Pont and Haupt (2010), Puissant et al. (2010), Bernabé et al. (2013), Gil et al. (2012), Vialard (2013) and Hermosilla et al. (2014). Other spatial partitions like regular grids have been adopted in the study of urban form for climatological purposes (Long and Kergomard, 2005). Despite differences in the final goal and spatial unit choice, these methodologies share a similar multivariate approach based on the cross-analysis of different dimensions of urban form components, like their density and geometrical properties. Typo-morphologies of city blocks represent the main outcomes of these methodologies. Another important phase in geoprocessing of the urban fabric is the computer-aided algorithms used to identify different empirical typologies of city blocks: spacematrix diagrams (Berghauser-Pont and Haupt, 2010), k-means (Bernabé et al., 2013; Gil et al., 2012; Vialard, 2013) and decision trees (Hermosilla et al., 2014).

The integration of the pedestrian point of view in the analysis of urban form is nevertheless absent from all the aforementioned methods. First, streets are normally considered as the limits for spatial partitioning, rather than as the core of a fragment of the urban fabric. Second, typo-morphologies of urban blocks are identified through clustering approaches, applied to individual spatial units, considering their local geometrical descriptors without any reference to their spatial relations to neighbouring units that could be explored by pedestrians. Spatial analysis is limited here to an a posteriori visual/qualitative study of the clusters identified.
## The skeletal streetscape

Contrary to geoprocessing methods in urban fabric analysis, the impact of street design on human perception represents the main focus of environmental psychology studies of urban form. Within this domain, recent developments might present valuable insights for our study. Harvey and Aultman-Hall (2016) define and measure the streetscape of a simplified 3D model of the street for the study of human perception. More specifically, they focus on the *skeletal streetscape*, defined as the main geometric characteristics of buildings and trees along the streets (size, spacing, frequency, etc.). The skeletal streetscape provides the structural support onto which the *skin*, made of design details, is fitted, completing the description of the streetscape. The authors propose a set of indicators implemented through a geoprocessing approach describing the geometrical organization of façades along the streets. Like in geoprocessing of the urban fabric, streetscape indicators are combined for classification purposes. Once again, the spatial distribution of the identified patterns is assessed qualitatively by analysts.

The quantitative measurement of the skeletal streetscape allows the study of aspects related to the street-building interface, highlighted and debated by several architects and urban designers (Alexander et al., 1987; Gehl, 2010; Lynch, 1960). More recently, innovative procedures have been proposed to measure further constituents of streetscapes like vegetation (Dubey et al., 2016), sky view (Leduc and Chauvat, 2015) and façade colours (Nguyen and Teller, 2017, etc.). In conclusion, streetscape analysis, whether limited to the skeletal structure or extending to include other constituents, focuses on the street-building interface and ignores both the content of buildings and the urban fabric as a whole.

## Indications for a new computer-aided and human-centred analysis of the urban fabric

We have discussed the main computer-aided methods for the analysis of urban form, focusing on those aimed at the form of the urban fabric. We also highlighted their conceptual and methodological limits when it comes to integrating the pedestrian point of view. The methodological challenges underlying the goal defined in the ‘Introduction’ section are now pointed out.

Our first need is to define a new spatial unit putting the street at the centre of the analysis. From the street, pedestrians can perceive the surrounding elements of the urban fabric (buildings, open spaces, plots, trees). Urban form indicators should be conceived and redefined from this perspective. Measurements of skeletal streetscapes as proposed by Harvey and Aultman-Hall (2016) seem to be an interesting starting point. Nevertheless, other important aspects of the urban fabric, well-identified by classical urban morphology schools, should be integrated into the approach, always including the pedestrian point of view. Finally, the urban fabric will be defined as a morphologically attentive urban streetscape.

Moreover, the pedestrian perspective to define the urban fabric should not simply be reduced to the capacities of human vision. It is by moving from one street to another that pedestrians can experience the spatial variability or homogeneity of urban fabric characteristics. Each indicator, describing a geometrical aspect of urban form within a spatial unit, should be evaluated considering its spatial distribution and variation. Geostatistical analysis on street networks might then provide methodological solutions for the study of spatial heterogeneity/homogeneity of perceivable morphological characteristics and serve as the input for subsequent clustering models. Thus, the street network becomes the underlying
spatial support, as in street-based configurational approaches, but with a different role in the
very nature of ensuing analyses.

Finally, types of urban fabric defined as morphological streetscapes might be character-
ized by specific sub-groups of morphological patterns, which may vary from one subspace to
another. The homogeneity of a whole set of morphological descriptors, which is imposed by
traditional classification methods (k-means, hierarchical clustering, etc.), might represent a
methodological limitation.

Bearing in mind these indications, we will show in the following sections how well-established methodologies from geoprocessing, geostatistics and AI might converge in a new protocol of analysis of the urban fabric to provide a more appropriate integration of the pedestrian point of view.

Case study

In order to evaluate the new methodological protocol, we will test it on a real-world case
study: that of the French Riviera metropolitan area, in southern France (see Supplemental
Appendix Figure S1). With a population of more than one million inhabitants over 1500
km², this space represents a unique conjunction of natural and urban landscapes. First, the
topography, with an elevation ranging from sea level up to the 1700 m of the pre-Alps
/passing through differently sloped hills and valleys). Second, the socio-political and histor-
ical influences on urban planning. Traditional villages and suburban developments are
spread around three high-density urban areas. From east to west, we find: Monaco and
its skyscrapers, the most densely populated sovereign nation in the world; the urban agglom-
eration of Nice with a regular meshed core inspired by the Turin model (Graff, 2013)
surrounded by hilly and less tightly planned areas; the urban agglomeration of Cannes–
Grasse–Antibes characterized by land irregularity together with the car-centred sprawling
development of the last 50 years. The combination of all these elements produces a sequence
of urban centres and peripheral areas of different sizes and morphologies. This study area
will give us the opportunity to test our method and to identify different urban fabrics by a
bottom-up approach.

Urban fabric identification: A new methodology

MFA is a new methodology for the identification and characterization of different urban
fabrics within a large study area (see Supplemental Appendix Figure S2). After defining
appropriate goal-specific spatial units (1), it can be summed up as follows: (2.a) geoprocess-
ing protocols to calculate a matrix of morphological indicators of the urban fabric; (2.b)
identification of spatial associations of indicator values (local patterns) on a network con-
strained space; (2.c) clustering of local patterns through Bayesian methods. The outcomes of
MFA allow the identification of urban fabrics in large study areas (3.a) and the study of
their spatial organization (3.b).

A new spatial unit partition of urban space: The pedestrian point of view

As discussed in the first part of this paper, when analysing urban fabrics through geo-
processing, we first need to define the base spatial unit. In order to analyse the skeletal
streetscape, Harvey and Aultman-Hall (2016) selected the proximity space on the two sides
of each street centreline, where the relative disposition of the built-up façades is measured.
In her study of the block-face, Vialard (2013) also proposed a morphological description of
elements within a one-sided buffer, going from the street-edge to the block interior. Here, centrelines and street-edges assume a major value of geometrical reference for the description of the urban fabric. The interface between streets and buildings/plots is the starting point to measure specific street-based indicators like corridor effects, façade alignments, setbacks, etc. Other representations of the public space overlook this specific aspect and focus on different criteria (e.g. convex spaces of axial maps and derivatives), losing this geometrical reference. Hermosilla et al. (2014) also recognized the need to integrate morphological descriptors of street-related spaces to characterize different urban fabrics, but their analysis was still centred on the urban block, which encompasses the descriptors of its surrounding streets.

As highlighted in previous sections, Harvey's approach allowed a local description of streetscapes disregarding their spatial relations. Spatial contiguities between these street-centred fragments of urban space should be reintroduced to explore their spatial variability. Studying different issues from urban form, Okabe and Sugihara (2012) proposed a similar partition of urban space, namely network-based Thiessen tessellation, with the intent of redistributing socio-demographic data on the street network.

Inspired by these studies, we propose a new division of urban space. A generalization of Thiessen polygons is created around each street segment, to identify the portion of planar space conventionally served by each segment. Only the closest space to street-edges can be observed by pedestrians. A double-sided buffer approximating to visible space is selected from the Thiessen partition (Figure 1). The resulting partition, the proximity bands (PBs) considers different visual depths, depending on the specific aspect of the urban fabric under analysis ('Decomposing urban fabric components' section). Moreover, the connectivity of the underling street network is transferred to PBs, allowing the implementation of network-constrained spatial statistics ('Spatial patterns on networks: ILINCS' section). Finally, their

<figure><img src="image_31.png" /><figcaption>**Figure 1.** From the Street Network to the PB implementation (above): some examples (below).</figcaption></figure>
small size variability reduces the potential bias induced by the modifiable area unit problem in statistical analysis. In our study area, 99,562 PBs were identified with street segment lengths of between 4 and 300 m and an average conventional surface of 4680 m² when a 20 m band of visible space was considered (see Supplemental Appendix Table S1).

## Decomposing urban fabric components

Twenty-one indicators obtained through geoprocessing in GIS were calculated for each PB. Selection criteria of indicators are the relevance and complementarity of the information carried by each of them when describing the urban fabric following the theory of urban morphology analysis (Borie and Denieul, 1984) reinterpreted from the pedestrian point of view¹ (see Supplemental Appendix Figure S3).

**Network morphology** is analysed through the street segment *Length* and *Linearity* (or inversely, its *Windingness*) (Harvey and Aultman-Hall, 2016). Three indicators measure the *Local Connectivity* of the street network: each street junction is described by Boolean values that indicate the presence/absence of nodes of degree one (cul-de-sac), four (crossings) and other (mainly bifurcations). Their averages on each street segment are later used to identify areas characterized by tree-like networks or meshed street grids (as in Marshall, 2012). **Built-up morphology** is studied within a 50 m wide PB and is first characterized by the traditional building *Coverage Ratio* index (Berghauser-Pont and Haupt, 2010). This indicator is then enriched by information about *Built-up Type prevalence*. The building footprint area is used as a proxy for categorization of buildings (Sevtsuk, 2014). For the French Riviera case study, the footprint segmentation is defined as follows: 0–125 m² (independent small houses), 125–250 m² (large villas and small traditional multi-family buildings, which are particularly frequent in our study area), 250–1000 m² (townhouses and medium-sized blocks of flats), 1000–4000 m² (modern large blocks of flats, commercial, industrial or service buildings), greater than 4000 m² (mainly functionally specialized large buildings).² We calculate the *Specialization of Building Types* by considering the presence/absence of ordinary dwellings. *Building Contiguity* informs us whether the urban fabric is characterized by adjoining buildings linked to the compactness of the urban built-up environment at a very fine grain. The role of these two indicators in the characterization of the urban fabric is recognized by classical urban morphologists (Caniggia and Maffei, 1979; Pinon, 1991). **Site Morphology** measures the presence of steeply sloped surfaces in the 50 m PB.

**Network-Building Relationship** indicators describe the building geometry analysed in relation to the street segment. For this reason, they are computed on narrower PBs of 10 or 20 m. They include the *Street Corridor Effect* (Araldi and Fusco, 2016), as a ratio between the total length of parallel façades and the street segment (similar to Harvey and Aultman-Hall, 2016 and Vialard, 2013, only considering parallel façades); *PB Building Height* (Berghauser-Pont and Haupt, 2010); *Open Space Width; Height/Width Ratio* (Harvey and Aultman-Hall, 2016). The *Building frequency along the street network* informs us about the serial vision of different buildings along a street (Hallowell and Baran, 2013). Following the same approach, *Land ownership fragmentation* is used as an indicator of the **Network-Plot Relationship**. Finally, the relationship between street **Network and Site Morphology** is analysed through *Street Acclivity*. Plot morphology per se, although central to the study of morphogenetic processes (Caniggia and Maffei, 1979; Conzen, 1960) is considered less relevant for pedestrian perception. The operational definition of each indicator is given in Table 1.
<table><caption>**Table 1.** The 21 indicators matrix for urban fabric morphology.</caption><thead><tr><th>Urban fabric component</th><th>Indicator</th><th>Definition and implementation formulae</th><th>Proximity band width</th></tr></thead><tbody><tr><td rowspan="4">Network morphology</td><td>Street length</td><td>Street segments length between two intersections</td><td>L<sub>street</sub></td></tr><tr><td>Windingness</td><td>I-(Euclidean distance/ Network distance) between two intersection</td><td>I - L<sub>eucl.</sub>/L<sub>street</sub></td></tr><tr><td rowspan="3">Local connectivity</td><td>Average of the presence nodes of degree 1 (ND1)</td><td>Σ ND<sub>i</sub>[0, 1]/2</td></tr><tr><td>Average presence nodes of degree 4 (ND4)</td><td></td></tr><tr><td>Average presence nodes of degree 3, 5+ (ND35+)</td><td>Σ Si / Σ S<sub>built</sub></td></tr><tr><td rowspan="10">Built-up morphology</td><td rowspan="4">Prevalence of building types</td><td>(0:125] m<sup>2</sup> building surf./total built-up surf.</td><td rowspan="4">50</td></tr><tr><td>(125:250] m<sup>2</sup> building surf./ total built-up surf.</td></tr><tr><td>(250:1000] m<sup>2</sup> building surf./ total built-up surf.</td></tr><tr><td>(1000:4000] m<sup>2</sup> building surf./ total built-up surf.</td></tr><tr><td rowspan="2">PB coverage ratio</td><td>(4000: max] m<sup>2</sup> building surf./ total built-up surf.</td><td rowspan="2"></td></tr><tr><td>Built-up Surface/PB Surf.</td></tr><tr><td rowspan="2">Building contiguity</td><td>Weighted average of buildings frequency on built-up units</td><td>Σ S<sub>tot</sub>/Σ S<sub>PB</sub></td></tr><tr><td>Specialization of building types</td><td>Specialized building surf./ PB surf.</td><td>Σ S<sub>spec</sub>/Σ S<sub>PB</sub></td></tr><tr><td rowspan="4">Network-building relationship</td><td>Street corridor effect</td><td>Parallel façades length/ street length</td><td>L<sub>par.fac</sub>/L<sub>street</sub></td></tr><tr><td>PB building height H</td><td>Building volume/PB surface</td><td>Σ V<sub>built</sub>/Σ S<sub>built</sub></td></tr><tr><td>Open space width W</td><td>(PB surf. - built surf.)/ street length</td><td>(S<sub>PB</sub> - S<sub>built</sub>)/L<sub>street</sub></td></tr><tr><td>Height/width ratio</td><td>PB building height/open space width</td><td>H/W</td></tr><tr><td rowspan="3">Network-plot relationship</td><td>Building frequency along SN</td><td>Number of buildings/ street length</td><td>N<sub>build</sub>/L<sub>street</sub></td></tr><tr><td>Land ownership fragmentation along the street network</td><td>Number of plots/ street length</td><td>N<sub>plot</sub>/L<sub>street</sub></td></tr><tr><td>Surface slope</td><td>High sloped surf. (S&gt;30%)/ PB surface</td><td>Σ Sloped Surf<sub>i</sub>/Σ S<sub>PB</sub></td></tr><tr><td>Site morphology</td><td>Street acclivity</td><td>Avg. arct(slope) along the street centreline</td><td>E [arct(slope)<sub>i</sub>]</td></tr></tbody></table>

PB: proximity band.
## Spatial patterns on networks: ILINCS

The local value of an indicator for each spatial unit is not adapted to describe the wider spatial patterns that are typical of the urban fabric, the latter being characterized by the arrangement in space of urban components with consistent morphological values. Spatial continuity and aggregation are key factors for their significance, especially if we adopt the pedestrian point of view. Several authors have suggested clustering spatial units using the raw values of morphological indicators, for example through k-means (Bernabé et al., 2013; Gil et al., 2012; Vialard, 2013). Spatial relations between adjoining spatial units are then overlooked and clustering of spatial units does not necessarily lead to the identification of urban fabrics. Through Geo-SOM, Hamaina et al. (2013) introduced spatial constraints into clustering, but their use of Euclidean distances between buildings is not coherent with our pedestrian viewpoint of the street network. We thus propose a preliminary phase of the detection of patterns for each morphological indicator, considering the spatial distribution of its values.

Local indicators of spatial association (LISA; Anselin, 1995) characterize the spatial autocorrelation of values around each unit using a local computation of Moran's Indicator. We propose the application of their network-constrained version (ILINCS, Yamada and Thill, 2010) to morphological indicators at the street level. For each spatial unit i, $w_{ij}$ describes local contiguities j on a street network

$$Local I_i = \frac{n(p_i - \bar{p}) \sum_j w_{ij}(p_j - \bar{p})}{\sum_i^n (p_i - \bar{p})^2} \quad (1)$$

where $p_i$ is the local value of a given variable in i and $\bar{p}$ is the average value in the study area. Araldi and Fusco (2016) discussed the application of LISA and ILINCS to the analysis of urban morphology indicators, showing how ILINCS is best fitted when analysing patterns from the pedestrian point of view. Despite the reduction in the variability of the spatial unit size (Table 1), ILINCS patterns might still be biased. In another study, Fusco and Araldi (2018) focused on the study of the statistical distribution of morphological indicators to propose appropriate variability corrections. To consider the heteroscedasticity of morphological rates, empirical Bayesian corrections (Assunção and Reis, 1999) were applied.

Through these network-constrained geostatistical analyses, we pass from a collection of morphological values for each spatial unit to statistically significant spatial patterns that correspond to hot- and cold-spots of given morphological characteristics, using the usual categories of LISA analysis (High-High, Low-Low, Low-High, High-Low and Not-Significant, see Supplemental Appendix Figure S4). These geostatistical classifications are the result of the analysis of the values of each PB, considering the local neighbourhood connected through the street network, with a topological depth of 3 units.

## Decomposing and characterizing urban fabrics: Bayesian clustering

Our geostatistical analysis produced 21 sets of significant spatial patterns. The next step consists of studying how these overlap to yield specific combinations. The urban fabric from the pedestrian point of view is precisely the perception of persistent co-occurrences of given morphological characteristics upon exploring urban space. At the same time, a few key characteristics could identify a given urban fabric even in the presence of a certain variability of other morphological aspects. For example, a specific compact and dense traditional
urban fabric could be characterized by consistently high building coverage ratio, street corridor effect, building contiguity and height/width ratio, while some heterogeneity of building types and network connectivity is seen. Clustering approaches aiming to achieve high intra-cluster homogeneity based on variance minimization of the values of all the indicators (like k-means, SOM and geo-SOM) are not well fitted to this task.

Bayesian clustering is applied here. This methodology is based on the Bayes' Theorem and it allows each spatial unit $PB_i$ to be described with a set of probabilities of belonging to each Urban Fabric class $UF_j$ considering its description given by the set of attributes $A_{1i}, A_{2i}, \dots, A_{ni}$ (morphological patterns)

$$p_{PBi}(UF_j|A_{1i}, A_{2i}, \dots, A_{ni}) = \frac{\prod_{k=1}^{n} p(A_{ki}|UF_j)p(UF_j)}{p(A_{1i}, A_{2i}, \dots, A_{ni})} \quad (2)$$

with the constraint

$$\sum_{j} p_{PBi}(UF_j) = 1 \qquad (3)$$

Two main reasons led to this choice: first, Bayesian clustering allows the identification of groups of individuals sharing a few key characteristics among the set of attributes $A_1, A_2, \dots, A_n$, which could vary from one group to another (as discussed in Fusco, 2016). Second, Bayesian probabilities can address the uncertain assignment of observations: hybrid street segments could have non-null probabilities of belonging to different urban fabrics. The careful theory-driven feature selection of our analysis justifies the use of a naive Bayesian classifier on the 21 morphological indicators.³

The combination of ILINCS and Bayesian clustering is a key feature of MFA. Instead of grouping streets depending on the similarity of their internal geometrical profile (row indicators on spatial units), we will thus perform Bayesian clustering of categorical values (HH, LL, HL, LH, NS),⁴ which, in turn, summarize the statistical significance of morphological spatial patterns defined on a street network.

## Results: Identification of the urban fabric families

Using eight different random seeds, 1000-step random walks explored the solution space to obtain optimal Bayesian clustering of spatial units. The search constraints were a minimum cluster content of 1% of the spatial units, a minimum average probability of 0.9 for the assignment of units to each cluster and a maximum of 20 clusters. The optimal solution is obtained by minimizing Akaike's (1974) information criterion, which combines the log-likelihood of the clustering solution given the data with penalization for the increasing number of clusters.

Within our eight random-walk searches, the nine-cluster solution was always found to be the optimum. A successive refining phase of the nine-cluster solution was carried out by imposing this fixed number of clusters to eight new random-walk searches. The results obtained were almost identical and the best one showing a contingency table fit score of 59.4%.

The projection of the 21 variables (resulting from geostatistical analyses) in mutual information space (see Supplemental Appendix Figure S5a) shows little redundancy among morphological indicators. Variables are well separated, with the exception of *Building Coverage*
<table><caption>**Table 2.** Indicator mutual information with cluster variable (left) and frequency of observation among the 10 collectively most informative variables (right).</caption><thead><tr><th>Indicator name</th><th>Mutual information (%)</th><th>Indicator name</th><th>Frequency of observation (%)</th></tr></thead><tbody><tr><td>Height/width ratio</td><td>37.68</td><td>Height/width ratio</td><td>100</td></tr><tr><td>Building coverage ratio</td><td>37.46</td><td>Building coverage ratio</td><td>97.13</td></tr><tr><td>Building frequency</td><td>33.88</td><td>Street acclivity</td><td>79.63</td></tr><tr><td>Building contiguity</td><td>29.27</td><td>Building frequency</td><td>75.61</td></tr><tr><td>Street corridor effect</td><td>28.22</td><td>Street length</td><td>72.01</td></tr><tr><td>Average building height</td><td>26.87</td><td>Buildings (0–125 m²)</td><td>64.18</td></tr><tr><td>Street length</td><td>21.80</td><td>Street windingness</td><td>61.70</td></tr><tr><td>Open space width</td><td>21.27</td><td>Buildings (1000–4000 m²)</td><td>59.10</td></tr><tr><td>Plot frequency</td><td>16.98</td><td>Average building height</td><td>51.56</td></tr><tr><td>Street windingness</td><td>16.34</td><td>Buildings (125–250 m²)</td><td>49.57</td></tr><tr><td>Street acclivity</td><td>15.83</td><td>Street corridor effect</td><td>47.91</td></tr><tr><td>Buildings (0–125 m²)</td><td>14.05</td><td>PB slope</td><td>43.15</td></tr><tr><td>Buildings (125–250 m²)</td><td>12.83</td><td>Plot frequency</td><td>41.13</td></tr><tr><td>Buildings (250–1000 m²)</td><td>12.46</td><td>Building contiguity</td><td>38.94</td></tr><tr><td>Buildings (1000–4000 m²)</td><td>11.31</td><td>Open space width</td><td>36.62</td></tr><tr><td>PB slope</td><td>8.90</td><td>Buildings (250–1000 m²)</td><td>35.57</td></tr><tr><td>Nodes 1</td><td>7.11</td><td>Nodes 1</td><td>11.77</td></tr><tr><td>Building Specialization</td><td>4.60</td><td>Building Specialization</td><td>9.59</td></tr><tr><td>Nodes 4</td><td>3.90</td><td>Buildings (S > 4000 m²)</td><td>4.31</td></tr><tr><td>Buildings (S > 4000 m²)</td><td>2.38</td><td>Nodes 4</td><td>3.19</td></tr><tr><td>Nodes 3, 5+</td><td>1.82</td><td>Nodes 3, 5+</td><td>0.00</td></tr></tbody></table>

PB: proximity band.

*Ratio, Building Contiguity, Height/Width Ratio and Building Frequency.* The latter also have the highest mutual information with the cluster variable (Table 2 – left). However, their redundancy does not play an important role in the clustering results. When asked to select the 10 collectively (rather than just individually) most informative variables for each spatial unit, the Bayesian classifier identifies the indicators listed in Table 2 – right, in descending order of frequency, and obtains a clustering accuracy of 93.14%. *Height/Width Ratio* and *Building Coverage Ratio* are thus normally coupled with *Street Acclivity*, *Building Frequency*, *Street Length* and the presence of *Small Buildings*, and identify the same urban fabric cluster obtained with all 21 indicators. Table 2 – right also tells us that the last five morphological indicators are seldom needed to correctly predict the cluster variable. The clustering results are relatively robust in terms of sensitivity to input data. The use of the 10 most informative variables on 10 test sets obtained in a 10-fold cross-validation schema produces a mean clustering accuracy of 92.62%, just half-a-point less than the one for the model learned on the whole data set.

Nevertheless, even the indicators that have the lowest mutual information with urban fabric clusters in general (Table 2 – left) or that are seldom used as the most informative variables (Table 2 – right) can have a non-negligible role in characterizing a given urban fabric, like the *PB slope* for cluster 6 or the frequency of *Nodes 4* for cluster 2 (see Supplemental Appendix Table S2).

Even more clearly, the projection of the clustering solution (see Supplemental Appendix Figure 5(b)) shows nine well defined clusters of urban fabrics (identified as UF1, UF2, etc.).
Their interpretation in terms of urban fabric characteristics (given by the Bayesian network in terms of probabilities, see Supplemental Appendix Table S2, Figure S6) goes beyond the scope of this methodological paper. We will briefly say that clusters 1, 2 and 3 correspond to the urban fabrics of the traditional compact city, cluster 4 to the modern discontinuous urban fabric, clusters 5 and 6 to residential suburban fabrics, clusters 8 and 9 to non-urbanized areas with sparse buildings and cluster 7 to a very specific connective artificial fabric, with sparse specialized buildings, bonding modern and suburban fabrics together to the traditional urban ones. Above all, when they are projected in geographic space, the clusters identify well-defined spatial patterns of connected spatial units defining the spatial extent of a given urban fabric type. Figure 2(a) represents the area around the city of Antibes. Street segments are assigned to the most probable class to which they belong. The old city centre is clearly distinguishable on the right, in a brown colour (UF1), surrounded by the traditional urban fabric of the late 19th and early 20th (UF2). More peripheral urban sectors belong to clusters 3 and 4, whereas vast suburban areas (UF5) border the city to the north and south, corresponding to the Antibes Cape. The traditional fabrics of smaller cities (here Vallauris) are islands within suburbia. Finally, the connective artificial fabric corresponds to retail developments around extra-urban roads and to the technical space around the port of Antibes. Similar results are found for the cities of the French Riviera (see Supplemental Appendix Figure S7) and these are consistent with expert knowledge of the study area (Graff, 2013). The contrast is striking with the nine-cluster solution being obtained with raw values of morphological indicators (see Supplemental Appendix Figure S8). Lacking any spatial constraint, the clustering procedure produces a mere typology of spatial units and fails to recognize that specific spatial arrangements of these typologies could be characterized as a unique urban fabric.

As we already mentioned in ‘Computer-aided approaches to urban morphology’ section, different objectives lead to different methodological choices in the analysis of urban fabrics. We assess the results obtained for the cities of the French Riviera as being particularly satisfactory with respect to the objective of identifying different urban fabrics potentially perceived by pedestrians moving on a street network, at the scale of a whole metropolitan area (Fusco and Araldi, 2017).

## Spatial organization of urban fabrics

In the previous section, the probabilistic results of Bayesian clustering were used for the identification and description of each class of urban fabric. In what follows, new insights about urban fabric organization within settlements are disclosed through the analysis of the spatial distribution of these probabilistic outcomes. These analyses provide quantitative support to the visual exploration of outcomes of urban form clustering. Moreover, they reveal the multi-scale nature of urban form upon passing from the street level (skeletal streetscape), to the neighbourhood level (urban fabric) and to the spatial organization within a metropolitan area.

## Where does the city end? Settlement boundaries and uncertainty

In the last few decades, several studies investigated indicators and techniques for measuring characteristics of settlements such as sprawl, compactness and contiguity. Nevertheless, a general consensus does not exist for the definition of urban boundaries and morphological agglomerations (Chaudhry and Mackaness, 2008; Parr, 2007; Tannier et al., 2011). Methods based on population distribution and density (Rozenfeld et al., 2008) or socio-economic
<figure><img src="image_32.png" /><figcaption>**Figure 2.** MFA outcomes and quantitative analysis of their spatial arrangement. (a) Projection in geographical space of MFA outcomes, (b) Zipf's regression of compact city agglomeration, (c) spatial distribution of compact-city centres, (d) variograms of urban fabrics and (e) example of cross-variograms relative to UF1.</figcaption></figure>

administrative partitions (Parr, 2007) mainly consider functional factors. If urban morphology is considered independently, other procedures have been proposed: a dilation-erosion procedure applied to the building footprint (Chaudhry and Mackaness, 2008) or fractal approaches applied to both raster and vector representation of the building surfaces
(Longley et al., 1991, 1992). A dilation method is the starting point of both these approaches: Minkowski dilation for Tannier et al. (2011); morphological mathematics in Benediktsson et al. (2003), Chaudhry and Mackaness (2008). The former investigates the variation in the number of aggregated elements at each dilation step in the overall space study, while the latter combines the dilation procedure with subsequent erosion. Both methods are applied to a Euclidean space and to elementary components (buildings, built-up pixels, etc.) without any semantic characterization of their urban content.

We were motivated by the same underlying principles as described in ‘Computer-aided approaches to urban morphology’ section and propose here the application of the aforementioned dilation-erosion procedure to the MFA probabilistic outcomes, while respecting network contiguities. MFA results are used for the delineation of the boundaries of the traditional compact city. We will first use the semantic information of our urban fabric types and select clusters 1, 2 and 3 as sharing similar characteristics specific to the traditional urban compact environment. We will filter PBs for which $p(UF1)+p(UF2)+p(UF3)\ge 0.8$ to focus on those elements that were assigned to the compact city with relatively low uncertainty. Applying topological dilation-erosion to networks of selected spatial units, we can identify urban spaces where compact city characteristics might be considered as belonging to the same urban continuum, thus defining compact city borders (Figure 2(a) to (c)).

Moreover, the count of the number of street segments in each resulting aggregation describes the hierarchy of compact urban settlements within the French Riviera. Zipf’s regression on $log(S_i)$–log(Rank$_i$) shows a regression coefficient of 1.04 with a $R^2$ value of 0.97 (Figure 2(b) and (c)). Despite the different implementation technique used, this value is close to the 1.095 obtained at the national scale by Oliveira et al. (2018). This observation seems to endorse the universal validity of Zipf’s Law as defended in Jiang et al. (2015). A discussion of the validity of Zipf’s Law for urban settlements transcends the goal of this paper. What we intended to show here is how MFA results could be used to identify and study borders of traditional settlements within a wider metropolitan area as well as their spatial organization at the regional scale with a network-based morphological approach.

## From quantitative outcomes to the archetyp model

Correlography and variography represent quantitative approaches that are usually applied in geology and ecology for the analysis of the spatial organization of phenomena under study (Legendre and Legendre, 1998). The former investigates and tests the presence of significant spatial autocorrelation in the data set, while the latter allows the description of its spatial organization and the observed structure to be related to hypothesized generation processes.

‘Spatial patterns on networks: ILINCS’ section described the adoption of autocorrelation analysis to identify statistically significant spatial patterns for each morphological indicator. The classification outcomes identify subspaces with a specific combination of spatial autocorrelation values by construction. Variography is adopted in this section, to quantitatively describe the spatial arrangement of urban fabrics throughout the network.

Within the Regionalized Variables Theory, empirical semi-variograms (called variograms for simplicity) are geostatistical functions conceived by Matheron (1965) to describe spatial phenomena combining the structural factor with a random noise component. In this theory, fluctuations of spatial distributions are considered to be a characteristic of the phenomena with a specific structure at a given scale, rather than statistical noise. Variograms quantify the similarity between the value observed at a point *i* and those observed at increasing distances within its surrounding area. The final aim of these functions is to quantify the
zone of influence of a phenomenon. Variogram function $\gamma(d)$ is defined by the following equation

$$ \gamma(d) = \frac{1}{2W} \sum_{h=1}^{n} \sum_{i=1}^{n} w_{hi}(y_n - y_i)^2 \quad (4) $$

where $\gamma$ decomposes the spatial variability of the observed variable among distances $d$. $W$ is the matrix of weight describing the spatial relation between values $y_i$ and the surrounding values $y_h$ at distance $d$.

The application of variograms could reveal the spatial structures of the study field, the presence of nested structures, discontinuities, anisotropies and the size of the influence zone of phenomena (Dauphiné and Voiron-Canicio, 1988). An interesting variation of the variogram is the cross-variogram, which focuses on the co-variance of two phenomena in space. Equation (1) is reformulated as

$$ \gamma_{xy}(d) = \frac{1}{2W} \sum_{h=1}^{n} \sum_{i=1}^{n} w_{hi}(x_n - x_i)(y_n - y_i) \quad (5) $$

Here, we will use the probabilistic outcomes of Bayesian clustering as an input of variography in order to disentangle the complex spatial arrangement of urban fabrics. A network-constrained topological variogram is implemented where $y_i$ represents the probability that a PB will be associated to a UF of class $j$, $p(PB_i = Class_j)$, while the weight matrix $W$ is determined by network topological contiguities.

From empirical variograms applied to each probability distribution (Figure 2(d)), two pieces of information can be retained: the *range* is the distance at which data are no longer correlated. This informs us about the extent of each *urban fabric field* (similarly to the concept of *urban field* in Longley et al., 1991), which could be used to characterize homogeneous morphological regions made up of a single urban fabric type. The *sill* is calculated as the limit of the variogram tending to infinity distances and informs us about the relative compactness/dispersion of the spatial distribution of each urban fabric in the metropolitan area. Higher $\gamma(d)$ values detect strong dissimilarities between association probability values at a given distance $d$ and they characterize those morphological regions with larger extents combined with spatial fluctuations (namely suburbs made of urban fabrics 5 and 6 but that integrate fragments of different types). Vice versa, smaller, compact spatial distribution are identified by lower $\gamma(d)$ values (as for UF 1, 2 and 3).

$\gamma_{xy}$ cross-variograms inform us about the spatial co-variation of two urban fabric types at increasing distances. As Bayesian probabilities add up to one for each street segment, the resulting function $\gamma_{xy}$ applied to each couple of urban fabrics is always negative. The relative distance from x-coordinates detects which urban fabrics surround the one analysed (x) at increasing distances. In see Supplemental Appendix Figure S7(b), for example, cross-variograms for the traditional urban fabric F1 are illustrated. This graphical representation of the relative spatial organization could answer three questions: (a) which UF comes first when we leave UF1? The order of $|\gamma_{xy}|$ represents the most probable UF to be found in the surrounding space at a given distance $d$ from the UF studied (for UF1: UF2 and UF3 at small distances, UF5 at larger ones); (b) at which distance are neighbouring UF5s located? The inverted bell described by $\gamma_{xy}$ identifies the minimal and maximum radii of the disks where each UF will most probably be found; (c) how similar is the analysed UF to the others? The
max of $|\gamma_{xy}|$ is limited for similar urban fabrics. The connective urban fabric (UF7) is an exception. It is organized in the form of ribbons and does not produce patches or disks around other morphological regions.

The information extracted by variograms and cross-variograms helps analysts in the generalization of urban fabric organization of the study area. This results in a proposal of the archetypical model of urban settlements in the French Riviera (see Supplemental Appendix Figure 9; see Fusco and Araldi, 2017 for a more detailed description).

## Conclusions and perspectives

This paper presented MFA, a new methodology for the analysis of the urban fabric, and showed how to exploit MFA results in the study of a vast metropolitan area. MFA is characterized by three main methodological innovations. First, the definition of morphological indicators for PBs, integrating the pedestrian point of view of the urban fabric. Second, geostatistical analysis is used to transform quantitative local measurements in categorical network-based patterns that are later used as inputs for clustering. Third, unlike more traditional approaches, Bayesian clustering considers specific subsets of indicators for each class identified. The method proved successful in the identification of urban fabrics and their spatial arrangements in the real-world case of the French Riviera metropolitan area.

Bayesian calculus was also used to assess the capacity of optimal subsets of morphological indicators to produce the same clustering results. The calculus cannot be used to identify missing variables. Only a qualitative assessment of results can provide hints in this direction. For example, the integration of vegetation into the characterization of urban forms could allow finer distinctions within urban and suburban fabrics. Another advantage of Bayesian clustering is its probabilistic content, which has been exploited in this analysis of spatial structures within the metropolitan area. Probabilities could also be used to assess uncertain characteristics of some urban fabrics and/or the uncertain assignment of street segments to urban fabrics.

The use of street PBs to study the urban fabric might bring new perspectives to the analysis of pedestrian-related phenomena. The contextual information resulting from MFA could represent the input for further analysis such as geographically weighted regression, hedonistic and multilevel modelling that is traditionally implemented in domains like walkability, retail, crime or environmental psychology.

The combination of geostatistics and Bayesian clustering represents an innovative procedure which is not limited to our definition of spatial units. These two approaches could also be applied to other spatial partitions, more appropriate for other research goals (i.e. block-, plot- or building-based partitions). This would allow traditional urban fabric analysis to integrate both the study of spatial heterogeneity of morphometric indicators and their specific combinations in defining urban forms.

In the second part of the paper, mathematical morphology and variography, both adapted to urban street networks, were implemented on MFA outcomes. These methods represent two valid procedures for a quantitative analysis of the spatial organization of urban fabrics. Several topics could be investigated, like compact city limits, urban fabric fields and arrangements within settlements. Through these procedures, MFA makes a contribution to the long-felt need for more clarity in the identification of morphological regions (Larkham and Morton, 2011).

In our analyses of the physical city's form, we drew a careful distinction between the study of the urban fabric and that of the configurational properties of street networks. MFA is devoted solely to the former. We envisage the convergence of the urban fabric analysis
and configurational analysis as a future research perspective to better understand urban space. After all, streets represent ‘the strongest and most familiar connection’ between these two approaches of urban morphology studies (Kropf, 2017). The street network-based partition of MFA shares the same primal representation as MCA in configurational studies. Their combination is therefore easily achievable. Some adaptation is needed when results from MFA and SSx are combined. This convergence could also be the most promising perspective in the study of form–function relations in urban space.

**Declaration of conflicting interests**

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

**Funding**

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was carried out thanks to a research grant of the Nice Côte d'Azur Chamber of Commerce (CIFRE Agreement Number 2015/1478 with UMR ESPACE).

**Notes**

1. The set of indicators here presented could be adapted to the specificities and data availability of each case study: Araldi et al. (2018) presented new geoprocessing algorithms for indicators, showing the versatility and adaptability of the MFA method.

2. Going beyond this expert based-approach, Perez et al. (2018) proposed to identify building types though Bayesian clustering of building morphological descriptors.

3. In a more exploratory data-mining approach, multi-step Bayesian clustering with latent factors should be preferred to counter strong variable redundancy (Fusco, 2016).

4. In MFA, HL segments are normally associated to LL patterns and LH segments to HH patterns to integrate exceptional units within a neighbouring prevalent urban fabric.

**Supplemental material**

Supplemental material for this article is available online.

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**Alessandro Araldi** is an Engineer in Environment and Planning (Politecnico di Milano, Italy) and Urban Geographer (University of Nice, France). PhD candidate, member of the ESPACE laboratory CNRS-University Côte-d'Azur (France) and Spatial Data Analyst at the Chamber of Commerce of Nice Côte-d'Azur (CIFRE agreement). His research activities focus on Urban Morphology and Urban Geography Analysis and Modeling with quantitative and innovative approaches.

**Giovanni Fusco** is a Senior Research Fellow at the French National Centre of Scientific Research (CNRS). After his PhD in Geography (University of Nice, France) and in Urban Planning (Politecnico di Milano, Italy), he has been Associate Professor of Geography and Planning at the University of Nice from 2004 to 2012. He joined CNRS in 2012 and he is presently Deputy Director of the ESPACE laboratory in Nice, France. His main research interests intersect issues of urban geography and urban morphology with methodological innovation in spatial analysis and modelling.
VACANT LANDS ON FRENCH MEDITERRANEAN COASTLINES: INVENTORY, AGRICULTURAL OPPORTUNITIES, AND
PROSPECTIVE SCENARIOS

Casanova Enault L. (MCF, Avignon Université), Popoff T. (Ingénieure, ESPACE), Debolini M.

2021, Land Use Policy, Volume 100, 2021, 104914, ISSN 0264-8377

https://doi.org/10.1016/j.landusepol.2020.104914

Cet article propose une méthodologie originale d'élaboration de scénarios de prospective territoriale alternatifs au modèle de développement touristico-résidentiel à l'œuvre sur le littoral méditerranéen. L'article étudie précisément les deux intercommunalités varoises de Toulon Provence Métropole (TPM<fnref n="15" />) et du Golfe de Saint-Tropez (GST<fnref n="16" />). Ces territoires sont concernés par une forte attractivité touristico-résidentielle qui a pour conséquence une dynamique soutenue d'artificialisation des espaces, réalisée préférentiellement en extension résidentielle (500 ha artificialisés au sein de TPM entre 2003 et 2011, 750 ha artificialisés au sein de GST entre 2003 et 2014). Ce modèle de développement touristico-résidentiel, par la surexploitation des ressources et les rejets qu'il induit, contribue à la fragilisation de l'écosystème littoral et à la perte irréversible d'espaces agricoles et naturels. Or, des alternatives sont possibles à la condition qu'elles soient soutenues par des politiques publiques volontaristes en la matière.

Cet article s'inscrit dans le projet scientifique de l'action de recherche intersites « Littoral » et est issu du programme « DURB<fnref n="17" /> » (La dépendance à l'urbanisation des territoires littoraux : existe-t-il des alternatives au tout résidentiel ?) dirigé par Guilhem Boulay, UMR ESPACE et conduit de 2017 à 2019.

L'originalité de l'article réside dans la méthodologie mise en œuvre. **Premièrement**, une méthode a été développée pour identifier les gisements fonciers au sein des deux intercommunalités étudiées à partir d'une exploitation des données des Fichiers Fonciers (CEREMA<fnref n="18" />) à l'échelle de la parcelle cadastrale. **Deuxièmement**, la qualification de l'opportunité de valorisation du gisement foncier pour une valorisation agricole a été établie, à partir d'une méthode mixte fondée sur l'analyse des caractéristiques des parcelles à partir d'un jeu d'indicateurs spatiaux et sur une analyse à dires d'experts. **Troisièmement**, des scénarios prospectifs spatialisés ont été élaborés selon une approche multi-échelles : à partir du choix d'un modèle de développement agricole à l'échelle du territoire (maraîchage et horticulture en circuit-court, mixité entre viticulture et arboriculture), ont été sélectionnées les parcelles susceptibles de répondre au mieux à ce projet. La réflexion s'est appuyée sur deux opérations de concertation : l'une menée dans le cadre de travaux dirigés du master 2 GEOTER<fnref n="19" /> d'Avignon Université (promotion 2018-2019), l'autre menée dans le cadre d'ateliers de concertation avec des acteurs locaux, dont la démarche et les enseignements ont été restitués dans un article paru dans la revue Netcom<fnref n="20" /> en 2022. La spatialisation des scénarios donne à voir une image future possible de ces territoires, qui pourrait se concrétiser si les décideurs locaux décidaient de mener une action volontariste en faveur d'un développement territorial plus soutenable. **Enfin**, les retombées socio-économiques de chaque scénario ont été mesurées à travers trois indicateurs évaluant les impacts en matière de création de surfaces agricoles cultivées, de création d'emplois dans ce secteur, et de retombées économiques issues des productions agricoles.

Cette publication témoigne par ailleurs de l'étroite collaboration de recherche menée avec l'INRAE<fnref n="21" />, par l'intermédiaire de Marta Debolini, chargée de recherche à l'INRAE, membre du projet DURB et co-auteur de l'article. L'article montre également la capacité du laboratoire à intégrer des jeunes diplômés de Master dans un projet de recherche et une publication internationale dans une revue de référence en géographie.

Cette publication constitue un jalon important de la recherche du laboratoire ESPACE dans trois domaines clés que sont le développement territorial des littoraux méditerranéens, l'élaboration de scénarios prospectifs spatialisés et l'analyse des dynamiques du foncier et de l'immobilier.

<footnote n="15">¹⁵ https://metropoletpm.fr/</footnote>

<footnote n="16">¹⁶ https://www.cc-golfedesainttropez.fr/</footnote>

<footnote n="17">¹⁷ http://www.ohm-littoral-mediterraneen.fr/spip.php?article223</footnote>

<footnote n="18">¹⁸ https://www.cerema.fr/fr</footnote>

<footnote n="19">¹⁹ https://univ-avignon.fr/rechercher-une-formation/master-geographie-geomatique-et-projets-territoriaux-1258.kjsp</footnote>

<footnote n="20">²⁰ https://journals.openedition.org/netcom/</footnote>

<footnote n="21">²¹ https://www6.paca.inrae.fr/emmah/</footnote>
# Vacant lands on French Mediterranean coastlines: Inventory, agricultural opportunities, and prospective scenarios

<figure><img src="image_33.png" /></figure>

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Laure Casanova Enault<sup>a,*</sup>, Tatiana Popoff<sup>a</sup>, Marta Debolini<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Littoral areas

Urbanization

Land planning

Agriculture potential

Var department

Prospective scenarios

Spatial analysis

## ABSTRACT

Mediterranean coastlines are undergoing a strong process of urbanization due not only to demographic growth, but also to the development of tourism. Moreover, municipalities have become more dependent on residential tax income, which creates a vicious circle for local urban planning. The Var department, in south-eastern France, can be considered as a relevaznt example of these dynamics, where residential urban sprawl is threatening existing vacant lands. At the same time, most of these territories still have a high natural value and agricultural potential. In this paper, we describe our inventory of vacant lands on a large territory (two intermunicipalities, which include 24 municipalities) at parcel scale, to give an innovative assessment of this phenomenon. We investigated non-residential development possibilities for existing vacant lands through possible pathways for sustainable planning projects. In particular, the impacts of increases in agricultural spaces were explored in the normative scenarios and were measured through socio-economic indexes. This work shows that there is still a large amount of vacant land (23.5% and 37% respectively for the two case studies) with different opportunities for agricultural valorization. It had to be taken into account that these lands are often on the fringe of urban areas, where the urbanization process is stronger. The possible alternative scenarios suggested here can be used as examples for policy makers, to encourage a different point of view regarding land use planning.

# 1. Introduction

## 1.1. MedCoast urban background

Mediterranean coastlines (hereafter MedCoast) have been experiencing strong, rapid urbanization during the last decades, partly due to demographic growth (from 90 million in 1950 to 290 million in 2005), and also related to urban and tourism development (UNEP, 2012; Burak et al., 2004). The Mediterranean area is also considered as a biodiversity hotspot (Cuttelod et al., 2008). Given the fact that an irreversible decrease in this resource is expected in response to climate change (see IPCC, 2014; Malcolm et al., 2006), attention has to be paid to the growing areas of artificialized lands (France Stratégie, 2019), which represent an additional threat to natural resources (Narducci et al., 2019, García-Ayllón, 2018b, Holon et al., 2015). While around 40 % of the MedCoast area is estimated to be artificialized in terms of land cover (UNEP, 2017), a major part of this is due to scattered residential urbanization. Urban sprawl has progressively increased in all European countries since the 1970s (Lagarias and Sayas, 2019; Salvati et al., 2013; Catalán et al., 2008; Kasanko et al., 2006), mainly at the expense

of agricultural areas, and has lead to fragmentation of the rural mosaic (e.g. Debolini et al., 2018; Aksoy et al., 2017; Salvati 2019; Salvati et al., 2012. In France, despite protective legislation for coastlines (Le Berre, 2016) and urban densification policies (to preserve both traditional city centers and natural lands), wide urban sprawl has developed (Pumain, 2004), and this process is continuing (Melot et al., 2018).

Nowadays, MedCoast areas are under pressure from different conflicting interests. This pressure is the main cause of land use competition, leading to land price rises and even to the subsequent exclusion of some social categories from land access (García-Ayllón, 2015; Coskun Hepcan, 2013). This competition has always favoured of urban residential use, because of the differential soil rent (Dachary-Bernard et al., 2011). For these reasons, MedCoast municipalities have progressively become more dependent on residential taxes (Grandclement and Boulay, 2016), with local policy makers increasingly interested in urban residential development (Membrado et al., 2016; Chabert, 2016). Urbanization is mainly taking place on natural or agricultural spaces, even though there is usually an officially-proclaimed intention to maintain them (Colsaet et al., 2018; Prévost and Robert, 2016; Padeiro, 2016; Abrantes et al., 2016). Such an urban residential development

<footnote><sup>a</sup> UMR ESPACE 7300 CNRS, Avignon University, Campus Hannah Arendt, Site Sainte Marthe, Bâtiment nord - 1E29, 74 rue Louis Pasteur, 84000 Avignon, France</footnote>

<footnote><sup>b</sup> UMR 1114 INRAE/Avignon University, EMMAH, Avignon, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: laure.casanova@univ-avignon.fr (L. Casanova Enault).</footnote>