# Modelling the Road Network as an Expression of Historical Spatial System Change in the Klaipeda Region (Lithuania)

Thomas Gloaguen, Kęstutis Zaleckis, Sébastien Gadal

► To cite this version:

Thomas Gloaguen, Kęstutis Zaleckis, Sébastien Gadal. Modelling the Road Network as an Expression of Historical Spatial System Change in the Klaipeda Region (Lithuania). GISTAM 2025, Apr 2025, Porto, Portugal. SCITEPRESS, pp.61-72, 2025, 978-989-758-741-2. 10.5220/0000189500003935. hal-05015289

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Distributed under a Creative Commons Attribution 4.0 International License
GISTAM 2025

11th International Conference on Geographical Information
Systems Theory, Applications and Management

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PROCEEDINGS

Porto, Portugal

1 - 3 April, 2025

EDITORS

Richard Lucas

Lemonia Ragia

https://gistam.scitevents.org

SPONSORED BY

<figure><img src="image_3.png" /></figure>

PAPERS AVAILABLE AT

<figure><img src="image_4.png" /></figure>
GISTAM 2025

Proceedings of the
11th International Conference on
Geographical Information Systems Theory,
Applications and Management

Porto - Portugal

April 1 - 3, 2025

Sponsored by

INSTICC - Institute for Systems and Technologies of Information, Control and Communication

In Cooperation with

ISPRS - International Society for Photogrammetry and Remote Sensing

EUROGEO - European Association of Geographers

SIFET - Italian Society for Photogrammetry and Topography

ASPRS - The Imaging and Geospatial Information Society
EuroSDR

RSPSoc - Remote Sensing and Photogrammetry Society

European Umbrella Organisation for Geographic Information

CaGIS - Cartography and Geographic Information Society
Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

Edited by Richard Lucas and Lemonia Ragia

Printed in Portugal

ISSN: 2184-500X

ISBN: 978-989-758-741-2

DOI: 10.5220/0000189500003935

Depósito Legal: 542044/25

https://gistam.scitevents.org

gistam.secretariat@insticc.org
# BRIEF CONTENTS

<table><tr><td>INVITED SPEAKERS</td><td>IV</td></tr><tr><td>SPECIAL SESSION CHAIRS</td><td>IV</td></tr><tr><td>ORGANIZING COMMITTEES</td><td>V</td></tr><tr><td>PROGRAM COMMITTEE</td><td>VI</td></tr><tr><td>SELECTED PAPERS BOOK</td><td>VII</td></tr><tr><td>FOREWORD</td><td>IX</td></tr><tr><td>CONTENTS</td><td>XI</td></tr></table>
INVITED SPEAKERS

**Khairul Nizam Abdul Maulud**
Universiti Kebangsaan Malaysia
Malaysia

**Anthony Lehmann**
University of Geneva
Switzerland

**Mihai Daniel Nita**
Transilvania University of Brasov
Romania

SPECIAL SESSION CHAIRS

SPECIAL SESSION ON S34I - FROM THE SKY TO THE SOIL

Krištof Oštir, University of Lubliana, Slovenia

Ana Teodoro, Oporto University, Portugal
# ORGANIZING COMMITTEES

**CONFERENCE CHAIR**

Lemonia Ragia, Hellenic Open University, Greece

**PROGRAM CHAIR**

Richard Lucas, Aberystwyth University, United Kingdom

**SECRETARIAT**

Vitor Pedrosa, INSTICC, Portugal

**GRAPHICS PRODUCTION AND WEBDESIGNER**

Inês Teles, INSTICC, Portugal

**WEBMASTER**

João Francisco, INSTICC, Portugal
Carolina Ribeiro, INSTICC, Portugal
PROGRAM COMMITTEE

**Fernando J. Aguilar**, Universidad de Almería,
Spain

**Bharath Aithal**, Indian Institute of Technology
Kharagpur, India

**Akhtar Alam**, University of Kashmir, India

**Manuel Arbelo**, Universidad de La Laguna, Spain

**Thierry Badard**, Laval University, Canada

**Pete Bettinger**, University of Georgia,
United States

**Sytze Bruin**, Wageningen University and Research,
Netherlands

**Ansgar Brunn**, Technical University of Applied
Sciences Würzburg-Schweinfurt, Germany

**Daniela Carrion**, Politecnico di Milano, Italy

**Ling Chang**, University of Twente, Netherlands

**Filiberto Chiabrando**, Politecnico di Torino-
DAD, Italy

**Eliseo Clementini**, University of L'Aquila, Italy

**Antonio Corral**, University of Almería, Spain

**Mattia Crespi**, Sapienza Università di Roma, Italy

**Vincenzo Di Pietra**, Politecnico di Torino, Italy

**Anastasios Doulamis**, National Technical
University of Athens, Greece

**Gianfranco Fornaro**, National Research Council
of Italy, Italy

**Giovanni Forte**, University of Naples Federico II,
Italy

**Ismail Karas**, Karabük University, Turkey

**Marcin Kulawiak**, Gdansk University of
Technology, Poland

**Anthony Lehmann**, University of Geneva,
Switzerland

**Miguel R. Luaces**, Universidade da Coruña, Spain

**Vladimir Lukin**, Kharkov Aviation Institute,
Ukraine

**Ron Mahabir**, George Mason University,
United States

**Jean Mas**, Universidad Nacional Autónoma de
México, Mexico

**Khairul Nizam Abdul Maulud**, Universiti
Kebangsaan Malaysia, Malaysia

**Helmi Zulhaidi Mohd Shafri**, Universiti Putra
Malaysia, Malaysia

**Marco Moreno-Ibarra**, Instituto Politecnico
Nacional, Mexico

**Gintautas Mozgeris**, Vytautas Magnus University,
Lithuania

**Zahn Munch**, University of Stellenbosch,
South Africa

**Shailesh Nayak**, National Institute of Advanced
Studies, India

**Anand Nayyar**, Duy Tan University, Vietnam

**Rouzbeh Nazari**, University of Memphis,
United States

**Francesco Nex**, University of Twente, Netherlands

**Evlyn M. L. M. Novo**, Instituto Nacional de
Pesquisas Espaciais, Brazil

**Mahesh Pal**, National Institute of Technology,
Kurukshetra, India

**Christian Prehofer**, DENSO Automotive
Germany, Germany

**Tarmo Remmel**, York University, Canada

**Rosanna Rivero**, University of Georgia, Design,
United States

**Mathieu Roche**, Cirad, France

**Jose Rodriguez-Avi**, University of Jaén, Spain

**Erick Sánchez Flores**, Universidad Autónoma de
Ciudad Juárez, Mexico

**Angela Schwering**, University Münster, Germany

**Diego Seco**, Universidade da Coruña, Spain

**Himan Shahabi**, University of Kurdistan,
Iran, Islamic Republic of

**Sulochana Shekhar**, Central University of Tamil
Nadu, India

**Yosio Shimabukuro**, Instituto Nacional de
Pesquisas Espaciais, Brazil

**Francesco Soldovieri**, Consiglio Nazionale delle
Ricerche, Italy

**Cornelis Stal**, HOGENT University of Applied
Sciences and Arts, Belgium
**Khairul Tahar**, Universiti Teknologi MARA, Malaysia

**Ana Teodoro**, Oporto University, Portugal

**Cláudia M. Viana**, University of Lisbon, Portugal

**Benoit Vozel**, University of Rennes I - IETR/Shine, France

**Huayi Wu**, Wuhan University, China

**Jing Yao**, University of Glasgow, United Kingdom

**Le Yu**, Tsinghua University, China

SELECTED PAPERS BOOK

A number of selected papers presented at GISTAM 2025 will be published by Springer in a CCIS Series book. This selection will be done by the Conference Chair and Program Chair, among the papers actually presented at the conference, based on a rigorous review by the GISTAM 2025 Program Committee members.
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# FOREWORD

This book contains the proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management. This year, GISTAM is held in Porto, Portugal, on April 1-3, 2025. The conference was sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). GISTAM 2025 was also organized in cooperation with the International Society for Photogrammetry and Remote Sensing, the European Association of Geographers, the Italian Society for Photogrammetry and Topography, the Imaging and Geospatial Information Society, the EuroSDR, the Remote Sensing and Photogrammetry Society, the European Umbrella Organisation for Geographic Information and the Cartography and Geographic Information Society.

The International Conference on Geographical Information Systems Theory, Applications and Management aims at creating a forum for researchers and practitioners that can address new challenges in geo-spatial data sensing, observation, representation, processing, visualization, sharing and managing in relation to both information communication and technologies (ICT) and management information and knowledge-based systems. The conference includes original contributions of either a practical or theoretical nature with these addressing research or applications (either specialized or of an interdisciplinary nature) and addressing different aspects of geographic information systems and technologies.

GISTAM 2025 received 47 paper submissions from 27 countries, of which 30% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least two, but usually three or more, members of the International Program Committee, which is composed of established researchers and domain experts.

The high quality of the GISTAM 2025 program is enhanced by the keynote lectures delivered by distinguished speakers who are renowned experts in their fields: Khairul Nizam Abdul Maulud (Universiti Kebangsaan Malaysia, Malaysia), Anthony Lehmann (University of Geneva, Switzerland) and Mihai Daniel Nita (Transilvania University of Brasov, Romania). Furthermore, a tutorial was offered on relevant subjects for the audience of the conference.

The conference is complemented by a Special Session on S34I - From the Sky to the Soil, chaired by Krištof Oštir and Ana Cláudia Moreira Teodoro.

All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index.

Several awards that recognise the best contributions are conferred at the conference's closing session, with these based on the combined marks of paper reviewing (as assessed by the Program Committee), and the quality of the presentation (assessed by Session Chairs at the conference venue).

Authors of selected papers will be invited to submit extended versions for inclusion in a forthcoming book of GISTAM Selected Papers to be published by Springer, as part of the CCIS Series. Some papers will also be selected for publication of extended and revised versions in the special issue of the Springer Nature Computer Science Journal.

The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are reported here. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly competent team.

We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of GISTAM, details of which are available at https://gistam.scitevents.org.
**Richard Lucas**

Aberystwyth University, United Kingdom

**Lemonia Ragia**

Hellenic Open University, Greece
# CONTENTS

## INVITED SPEAKERS

### KEYNOTE SPEAKERS

Developing an IoT-Based Multi-Sensor System for Real Time Fire Detection and Spatial Analysis to Improve Campus Safety
*Khairul Nizam Abdul Maulud* 5

Nexus Approach for a Sustainable Environment: From Individual Modeling Approaches to Digital Twins
*Anthony Lehmann* 7

The Role of UAV LiDAR Sensors and Mobile Scanners in Producing Geo-Spatial Data in Complex Forest Ecosystems: Is Digital Twinning in Forestry a Myth or Reality?
*Mihai Daniel Nita* 9

## PAPERS

### FULL PAPERS

Are Londoners Getting Healthier?
*Yijing Li, Sijie Tan, Xiangbo Chang and Xiaohui Chen* 15

Navigating Points of Interest: The Dog-Walker Pathfinding Algorithm
*Natsuki Tsutsui and Shohei Yokoyama* 27

Characterizing Machine Guidance in Geospatial Analysis
*Yue Hao and Guoray Cai* 39

Satellite Images and Spectral Vegetation Indices as Auxiliary Tools to Monitoring Fuel Availability in Areas Prone to Wildfire: Study Case in the Northern Region of Portugal
*Bárbara Pavani-Biju, José G. Borges, Susete Marques and Ana C. Teodoro* 51

Modelling the Road Network as an Expression of Historical Spatial System Change in the Klaipeda Region (Lithuania)
*Thomas Gloaguen, Kęstutis Zaleckis and Sébastien Gadal* 61

Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City
*Carlo Angelo R. Mañago, Marielle G. Nasalita, Cesar V. Saveron, Ynah Andrea D. Sunga and Alexis Richard C. Claridades* 73

A Luminance-Based Lane Marking Candidate Quality Assessment for Autonomous Driving in GIS Contexts
*Pathum Rathnayaka, Young Hun Kim, In Gu Choi, Gi Chang Kim and Duk Jung Kim* 85

Person Detection from UAV Based on a Dual Transformer Approach
*Andrei-Stelian Stan, Dan Popescu and Loretta Ichim* 95

Analyzing Multitemporal Datasets to Monitor Topographic Changes in Rio Cucco Italy
*Jad Ghantous, Vincenzo Di Pietra, Elena Belcore and Nives Grasso* 103
SHORT PAPERS

<table>
  <tbody>
    <tr>
      <td>Differential Interferometric Synthetic Aperture Radar-Based Landslide Monitoring: A Case Study of Wayanad, India<br>Manvi Kanwar and Samsung Lim</td>
      <td>113</td>
    </tr>
    <tr>
      <td>Preservation and Protection of Cultural Heritage in High Tourism Areas Using GIS Technology: A Case Study of the Medieval City of Rhodes<br>Foteini-Pelagia Leventi, Lemonia Ragia and Dorina Moullou</td>
      <td>121</td>
    </tr>
    <tr>
      <td>ADAclassifier: Trying to Ascertain Why the Ground Is Moving<br>José A. Navarro, Anna Barra, María Cuevas-González, Pablo Ezquerro, Silvia Bianchini, Marta Béjar-Pizarro, Riccardo Palamà and Oriol Monserrat</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Unsupervised Image Classification Algorithms Applied to Fire-Prone Area Detection<br>I. Rahimi, L. Duarte and A. C. Teodoro</td>
      <td>136</td>
    </tr>
    <tr>
      <td>Urban Growth in Metropolitan Regions Using Dynamic Modeling by Cellular Automata: A Comparative Analysis Between Brazil and Portugal<br>Elizabeth Maria Feitosa da Rocha de Souza, Antonio Alberto Teixeira Gomes and Vandre Soares Viegas</td>
      <td>142</td>
    </tr>
    <tr>
      <td>Navigating Boundary Discrepancies in SAD69-Based Delimitation: A Case Study and Practical Guidelines<br>L. R. Costa, H. S. Delabary and R. Z. Araujo</td>
      <td>150</td>
    </tr>
    <tr>
      <td>Analysis of Surface Thermal Behavior in Different Local Climate Zones (LCZ): A Case Study in Bragança (Portugal) (2013-2024)<br>Cátia Rodrigues de Almeida, Artur Gonçalves and Ana Cláudia Teodoro</td>
      <td>157</td>
    </tr>
    <tr>
      <td>Hazard Modeling Using Sentinel Data for Risk Assessment and Management of Tsho Rolpa Glacier, Nepal<br>Prasil Poudel, Nikesh Budha Chettri, Nabin Sah and Drabindra Pandit</td>
      <td>165</td>
    </tr>
    <tr>
      <td>Planning Delivery Services: Depot Clustering Based on Socio-Economic Indicators and Geospatial Metrics<br>Iñaki Cejudo, Laura Rabadán, Eider Irigoyen and Harbil Arregui</td>
      <td>172</td>
    </tr>
    <tr>
      <td>Advancing Airport Land Subsidence Monitoring Through Time-Series InSAR Technology<br>Anuphao Aobpaet, Intira Thanomsin, Chanikan Yodya and Suchanpong Obnam</td>
      <td>179</td>
    </tr>
    <tr>
      <td>An Innovative Model Based on Carvalho Rodrigues's Entropy to Assess Governance in Africa: A Guinea-Bissau Case Study<br>João Serras, Paulo Morgado and Jorge Malheiros</td>
      <td>187</td>
    </tr>
    <tr>
      <td>LiDAR-Based 3D Reconstruction for Robotic Pipelines Inspection<br>Monika Sara Kawka, Lazaros Grammatikopoulos, Ilias Kalisperakis and Christos Stentoumis</td>
      <td>195</td>
    </tr>
    <tr>
      <td>Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine<br>Dan Abudu, Lucy Bastin, Katie Chong and Mirjam Röder</td>
      <td>203</td>
    </tr>
    <tr>
      <td>Low-Cost GNSS Receivers Reliability Using Centipede RTK Network for Land Surveying<br>Muhammad Ali Sammuneh, Mojtaba Eslahi, Rani El Meouche and Elham Farazdaghi</td>
      <td>211</td>
    </tr>
  </tbody>
</table>
The Impact of Data Science on Geography: A Review with Optimization Algorithms
Roberto de Oliveira Machado 219

Surface Current Visualization in Waterway Based on Mike 21 Model and S-100 Standards
Zixuan Wang, Mingyang Pan, Shaoxi Li, Chao Li and Zongying Liu 231

Assessment of Fine-Tuned Canopy Height Maps from Satellite Imagery: A Case Study in the Czech Republic
Leonidas Alagialoglou, Ioannis Manakos, Olga Brovkina, Jan Novotný and Anastasios Delopoulos 236

Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas
Frederick Kistner and Sina Keller 244

Remote Sensing-Based Temporal Analysis of Aletsch Glacier Retreat (1990–2020)
Andrija Krtalić, Ana Kuveždić Divjak and Kristina Zeman Šteković 253

Tracking the Progression of Burned Areas in Tropical Peat Swamp Forests by Integrating Sentinel Optical and SAR Imagery: A Case Study of Binsuluk Forest Reserve in Sabah, Malaysia
Nurul Aina Abdul Aziz, McKreddy Yaban, Muhamad Zulfazli Zakaria and Siti Atikah Mohamed Hashim 264

## SPECIAL SESSION ON S34I - FROM THE SKY TO THE SOIL

### FULL PAPERS

S34I Project: Secure and Sustainable Supply of Raw Materials for EU Industry
Ana Cláudia Teodoro, Joana Cardoso-Fernandes, Mihaela Gheorghe, Francesco Falabella, Fabiana Calò, Antonio Pepe, Delira Hanelli, Andreas Knobloch, Roberto De La Rosa, Fahimeh Farahnakian, Georgios Periklis Georgalas, Enoc Sanz-Ablanedo, Vaughan Williams and Krištof Oštir 277

An Ensemble Modeling Approach for Mapping Critical Mineral Distribution with LiDAR and PRISMA Data
Fahimeh Farahnakian, Mahyar Yousefi and Ana Cláudia Teodoro 286

Evaluation of EnMAP Hyperspectral Data for the Identification of Placers in the Rias Baixas Region (Spain)
Beatriz L. Araújo, Joana Cardoso-Fernandes, Antonio Azzalini, Morgana Carvalho, Alexandre Lima, Francisco J. González and Ana Cláudia Teodoro 297

AMD Mapping in the Lusatian Region: From Medium to Very High-Resolution R/S Data
Delira Hanelli, Enis Sterjo, Roberto De La Rosa and Andreas Knobloch 305

Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping
Roberto De La Rosa, Michael Steffen, Ina Storch, Andreas Knobloch, Joana Cardoso-Fernandes, Morgana Carvalho, Mercedes Suárez Barrios, Juan Morales Sánchez-Migallón, Petri Nygren, Vaughan Williams and Ana Cláudia Teodoro 317
SHORT PAPERS

Application of UAV, GNSS and InSAR Techniques in the Raw Material Supply Chain
Tanja Grabrijan, Krištof Oštir, Klemen Kozmus Trajkovski, Dejan Grigillo,
Veronika Grabrovec Horvat, Polona Pavlovčič Prešeren, Veton Hamza, Antonio Pepe, 331
Fabiana Calò, Francesco Falabella, Enoc Sanz-Ablanedo, Mihaela Gheorghe, Teodora Selea
and Ana Cláudia Teodoro

Exploring and Mapping Marine Placers in Vigo Estuary Shoreline Using GIS Cartographic Tools
Wai L. Ng-Cutipa, Ana Lobato, Francisco J. González, Georgios P. Georgalas, Irene Zananiri, 339
Joana Cardoso-Fernandes and Ana C. Teodoro

AUTHOR INDEX 347
INVITED SPEAKERS
<figure><img src="image_6.png" /></figure>
KEYNOTE SPEAKERS
<figure><img src="image_7.png" /></figure>
# Developing an IoT-Based Multi-Sensor System for Real Time Fire Detection and Spatial Analysis to Improve Campus Safety

Khairul Nizam Abdul Maulud

*Civil Engineering, Universiti Kebangsaan Malaysia, Malaysia*

**Abstract:** Since the year 2000, the smart city concept has been integrated into university campuses to address shared challenges in providing safe and comfortable environments for occupants. However, campuses face unique risks, such as high usage of paper-based products and combustible amenities, which can lead to severe fire incidents. Fire statistics from local university campuses have shown significant losses from such events. Conventional fire alarms on campuses have limitations, including a lack of real-time alerts during fire hazards and a focus on single sensing. Therefore, enhancing traditional fire alarms with a multi-sensor system is necessary. This study aims to develop an Internet of Things (IoT)-based sensor for real-time detection, alerting and notification of campus building stakeholders, utilizing IoT, wireless sensor networks (WSN) and a cloud database. The IoT-based sensor incorporates temperature, humidity, smoke, and flame sensors, with data stored in ThingSpeak, serving as a sensor visualization hub. Five simulations, based on fire ignition probabilities and building attributes, were conducted to assess the prototype's functionality, using geospatial tools for data analysis. Establishing a smart campus has become appealing with essential technologies such as IoT, WSN, cloud computing, and AI, leading to innovations in fire detection and safety measures integration. However, ensuring campus safety requires state-of-the-art sensors integrated with IoT technology and spatial-based analysis techniques. This study not only focuses on sensor development but also employs geospatial software analysis for spatial data quantification and identification of key sensor characteristics, aiding future analysis.

## BRIEF BIOGRAPHY

Sr Ts. Gs. Dr Khairul Nizam Abdul Maulud is an Asso Prof. at the Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM). He was also appointed as a Deputy Executive Director (Sustainability), UKM Strategy Center, Universiti Kebangsaan Malaysia. Previously, he was the Head of Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia. He joined Universiti Kebangsaan Malaysia in 2001 after completing his B.Sc. in Geoinformatics from the University of Technology, Malaysia. He received his M.Sc in Geoinformatics from the University of Technology, Malaysia and PhD in Civil & Structural from Universiti Kebangsaan Malaysia. Currently, he has published more than 151 journal papers, 44 books and book chapters in book and 99 proceedings in national and international proceedings. He has been involved as a principal researcher and consultant works for more than 100 research grants, 46 consultant works in Malaysia. To date, he has supervised 36 PhD students, 15 Master's Students and 38 bachelor's degrees. His expertise is a Geospatial Technology, GIS

and Geomatic and has almost 23 years of experience in research and consultancy. He has successfully completed almost 23 water-related projects including sea-level rise, geospatial analysis, landuse changes, water quality, shoreline erosion, water management using GIS and Coastal Vulnerability Index. He has received a Australia Awards Fellowship from Department of Foreign Affairs and Trade (DFAT) Australia and has undergone a fellowship program at the University of Technology Sydney, Australia. He now led high-impact research on climate change, especially on the physical and economic impacts. He also is an expert panel for the government of Malaysia, especially in coastal geomorphology, geospatial analysis and spatial water quality analysis. He is very active as an evaluation panel for research grants at the Ministry of Science, Technology and Innovation and the Ministry of Higher Education Malaysia.
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# Nexus Approach for a Sustainable Environment: From Individual Modeling Approaches to Digital Twins

Anthony Lehmann

*University of Geneva, Switzerland*

**Abstract:** Evidence-based environmental management relies heavily on high-quality, accessible geospatial data. Despite the widespread adoption of Open Data policies across Europe, challenges persist in discovering and accessing data across temporal and spatial scales due to technical, political, economic, and cultural barriers. Moreover, the complexity of natural systems generates vast and often unmanageable amounts of data. To address these challenges, the concept of Essential Variables (EVs) has been pivotal. Initially developed for Essential Climate Variables (ECVs), this framework has been adapted to define Essential Biodiversity Variables (EBVs) and, more recently, extended to the socio-ecological Earth system within a European project. Comprehensive monitoring of the Earth system requires robust Spatial Data Infrastructures (SDIs) to facilitate the dissemination of metadata and, ideally, analysis-ready data. However, the complexity and costs associated with SDI implementation—such as those observed in INSPIRE and GEOSS initiatives—pose significant obstacles, particularly in achieving seamless machine-to-machine data access through interfaces like APIs. In recent years, advancements in geospatial workflows have shifted the focus toward defining and sharing complex analyses. Standard programming languages like Python and R, coupled with open platforms like GitHub, have significantly improved workflow reproducibility and accessibility. Additionally, the availability of diverse computing backends—from personal computers to cloud-based systems—has enabled scalable geospatial analyses. We illustrate these developments with two case studies in Switzerland. The first involves defining the Swiss national ecological infrastructure through integrated workflows that combine species distribution modeling and ecosystem service assessments to prioritize landscapes, identifying the top 30% for conservation. The second focuses on hydrological modeling using the Soil and Water Assessment Tool (SWAT) to transform daily weather data into river water quality and quantity outputs. Despite their success, these examples highlight opportunities for further integration within a Nexus framework, connecting Climate-Food-Water-Energy-Ecosystem domains to identify trade-offs and synergies in addressing complex environmental issues. Ultimately, the integration of these approaches into Digital Twins for the environment represents a transformative step toward converting essential variables into actionable insights for informed environmental decision-making.

## BRIEF BIOGRAPHY

Prof. Anthony Lehmann is a specialist in the field of Species Distribution Modeling (SDM). He pioneered the development of GRASP, the first package enabling spatial predictions based on point observations of plant and animal distributions. Since its publication, GRASP has been cited over 400 times and applied across diverse terrestrial and aquatic ecosystems, contributing to more than 20 co-authored publications. GRASP laid the foundation for subsequent tools like MAXENT, BIOMOD, and CARET, and was instrumental in addressing the challenge of presence-only data in SDM. More recently, Prof. Lehmann has focused on leveraging hydrological modeling to inform decision-making processes. He initiated and coordinated the FP7 enviroGRIDS project, a four-year endeavor involving over 100 scientists

aimed at bridging the gap between scientific data and decision-making in the Black Sea catchment. This project advanced Earth Observation capacity building, data-sharing practices, and the calibration of a large, complex hydrological model for the region. Prof. Lehmann’s recent research emphasizes spatially explicit assessments of ecosystem services. He coordinated the H2020 ERA-PLANET/GEOEssential project (2017–2021), which developed geoprocessing workflows to link Earth Observation data with environmental policy indicators using Essential Variables. Building on this expertise, he led the Swiss National Fund project SWATCH (2017–2020), focusing on eco-hydrological modeling of Swiss rivers, and is currently involved in the ValPar.CH project (2020–2023), exploring ecosystem services, biodiversity, and eco-logical infrastructures in and around Swiss regional
parks.
# The Role of UAV LiDAR Sensors and Mobile Scanners in Producing Geo-Spatial Data in Complex Forest Ecosystems: Is Digital Twinning in Forestry a Myth or Reality?

Mihai Daniel Nita

Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Romania

**Abstract:** Complex forest ecosystems pose significant challenges for geo-spatial data acquisition due to dense vegetation, heterogeneous canopy structures, and occlusion effects. This presentation explores how drone-based LiDAR sensors and mobile terrestrial scanners can be synergistically employed to overcome these challenges and generate comprehensive, high-quality geo-spatial datasets. Focusing on different case study from several types of forests in Carpathian Mountains, we detail a dual approach where drones provide rapid, large-scale aerial coverage to capture canopy dynamics, while mobile scanners deliver precise, ground-level details crucial for mapping individual trees and understory features. By integrating these complementary sensor modalities, our workflow enables the development of detailed three-dimensional models and digital twins, supporting enhanced forest inventory assessments, improved monitoring of forest health, and refined estimations of carbon stocks. Preliminary results demonstrate that the fusion of aerial LiDAR data with terrestrial scanning not only mitigates the limitations of each individual technique but also paves the way for automated processing and sustainable forest management practices. This study focuses on methodologies, challenges, and future directions in leveraging advanced sensor technologies for efficient geo-spatial data production in complex environments such as forests.

## BRIEF BIOGRAPHY

I am Professor at Department of Forest Engineering, the Transylvania University of Brasov. My subjects are Remote Sensing and GIS (for master's degree) and Watershed Management and Hydrological Terrestrial Measurements (for bachelor's degree). As a researcher I am involved in projects regarding forest management and monitoring, mainly as RS-GIS expert on topics like LULUCF (land use, land-use change, and forestry) using remote sensing and drone mapping, modeling habitat distributions for species and evaluating Forest Ecosystem Services (FES). Throughout my journey, I've had the privilege of developing a diverse expertise that revolves around forestry, environmental management, geospatial analysis, and sustainable resource utilization. While I still have much to learn, I've been fortunate to be a part of various projects, research endeavors, and academic contributions that have allowed me to contribute to the understanding and conservation of our natural world.
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# Are Londoners Getting Healthier?

Yijing Li<sup>a</sup>, Sijie Tan, Xiangbo Chang and Xiaohui Chen

CUSP London, Department of Informatics, King's College London, U.K.

yijing.li@kcl.ac.uk, sijie.l.tan@kcl.ac.uk

**Keywords:** Obesity, Mental Health, London, Spatial Pattern.

**Abstract:** It utilised urban data from multiple sources, to map out the physical health and mental health patterns in London over space and time. On basis of recognising the spatial patterns trajectory changes, obesity among adults and children in London areas had been investigated with selected demographic, socio-economic and environmental factors, to identify the most influential factors in all, and for local community; similarly, workflow had been again designed to investigate the influential factors for mental health prevalence as well. Upon comparing the selected models, models considering neighbourhood spillover effect has been found to be the optimal, to identify significantly influential factors on urban health, such as age group, green space access, household deprivation, income deprivation and air quality. The findings underscore the necessity for targeted, location-specific public health interventions to effectively combat obesity; highlight the importance of spatial heterogeneity, offering detailed insights into regional variations; and suggest tailored strategies for public health policies. This work fills a critical gap and demonstrates the need for geographically informed public health strategies.

## 1 INTRODUCTION

As the capital of UK, health statuses for London residents have always been the prime topic, and could be interpreted from mainly two strands, the physical health and the mental health. The former strand has been found to be with “overweight and obesity represent probably the most widespread threat” to physical health (Department of Health and Social Care, 2011), requiring for leaders’ implementing public health measures to reduce obesity, such as the Mayor’s London Food Strategy to tackle with obesity and provide a framework for assessing the impact of these policies (PHE, 2020). The latter strand is a crucial component of human being’s overall well-being and is closely linked to physical health (NHS England, 2020), hence organisations such as PHE and the GLA regularly publish reports and datasets that for further investigations (Rosenberg, 2019). It also inspired empirical studies investigate the significant socioeconomic disparities among London, suggesting influential socioeconomic factors may drive the disparities among London regions (Mayor of London, 2018). However, there is a lack of evidence on status quo patterns of Londoners’ health: are they becoming

healthier over time? Are there any regional variations for London residents' health statuses, for either physical health or mental health? If so, what are the influential factors driving such differences?

To address above questions, this project aims to explore the latest obesity and mental health levels among London residents, on basis of multiple openly accessed datasets from varied sources, to map out the health spatial patterns changes in London in recent decade by age group, to investigate the driving forces from socio-economic contexts and environmental measures, so to serve the potential strategic policy adjustments among relevant stakeholders for healthcare equity and improvement in the city.

## 2 BACKGROUND AND EMPIRICAL STUDIES

### 2.1 London Demographics

London is formed by City of London and other 32 boroughs, further breakdown into 417 Middle layer Super Output Areas (MSOAs) or 4835 Lower Super Output Areas (LSOA) (ONS, 2011), with residents

<sup>a</sup> https://orcid.org/0000-0002-9831-0298

Li, Y., Tan, S., Chang, X. and Chen, X.

Are Londoners Getting Healthier?

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), pages 15-26

ISBN: 978-989-758-741-2; ISSN: 2184-500X

Proceedings Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Ltda.
around 8.9 million (ONS Census 2021, 2022), and is
well-known for its ethnic and cultural diversity in that,
residents in London speak more than 300 languages
and come from a wide range of ethnic backgrounds
(GLA, 2020). High level of population diversity
brings the mayor's manifesto commitment on a
"fairer, healthier and more equal" London (Mayor's
Office, 2022) become a prominent but challenging
topic, hereafter the necessity to understand
Londoners' status quo health levels.

## 2.2 Obesity as a Measure for Physical Health

As defined by WHO (2024), obesity is the excessive
accumulation of fat that poses a threat to health,
which commonly measured by body mass index
(BMI), where people with higher BMI may expose
to higher risk of obesity-related health
complications. From WHO's classification, adults
with obesity are normally categorised into Grade I
obesity (BMI at 30-34.9), Grade II obesity (BMI at
35-39.9), and Grade III obesity (also known as
morbid obesity, BMI at 40 or above); children's
obesity is defined by age-specific BMI percentiles at
or above the 95th percentile for their age and sex.

Individuals diagnosed with obesity were related
to potential health problems by their age groups,
stages of growth and development. For example,
children are normally thought to experience high
metabolic rate and rapid physical and psychological
changes (Stamatakis et al., 2010), with potentially
long-term health influences into their adulthood. On
the contrary, adults have a relatively lower metabolic
rate and a mature lifestyle and eating habits, hence
obesity developed during adulthood will be
associated with chronic diseases such as heart
disease and diabetes. Their living environments are
taken as influential to the development of obesity
symptoms. For instance, access to recreational
facilities, availability of healthy food, transport
infrastructure, and the built environment are
important shaping residents' lifestyles, then affect
local obesity rates. The city's extensive public
transport network, abundant green spaces and
walkability create a unique environment for
assessing the impact of urban design on physical
activity levels and eating habits (ONS, 2019). Recent
research on adult obesity has highlighted the
complex interplay of multiple influencing factors,
including socioeconomic status, dietary habits,
physical activity, and environmental conditions.
Stafford et al. (2010) enriches this discussion by
highlighting that, obesity trends are severe among

population from poorer socioeconomic backgrounds
in developed countries. For example, people with
lower incomes may face food insecurity, leading to
poorer diet quality and higher rates of obesity. These
findings reflect the broader claim that,
socioeconomic disadvantage is a key determinant of
health disparities. Focusing on environmental
factors, Shenassa et al. (2006) highlighted the
significant correlation between sedentary lifestyles
and obesity in Europe. Hobbs (2022) emphasized the
impact of the environment on the level of physical
exercise, which in turn affects the management of
obesity, thus showing the importance of public space
and parks. Furthermore, as highlighted by the 2018's
Health Survey for England (NHS England, 2019),
the availability of physical activity facilities versus
fast food options illustrates the environmental
opportunities and risks that contribute to obesity.
These environmental determinants of obesity
interact intricately with sociodemographic factors.

Children's development of obesity symptoms
was found to have varied but interrelated factors
from adults, which were mostly the home
environment, school policies, access to recreational
facilities, and nutrition education (Schratz et al.,
2023). The impact of parental obesity and family
eating habits is particularly strong in that, parental
obesity, especially maternal obesity, greatly
increases the risk of childhood obesity due to shared
genetic and environmental factors (Kral and Rauh,
2010). Stafford et al. (2010) found that children from
disadvantaged backgrounds, such as families from
lower incomes or manual labors, are at greater risk
of high obesity rates. Schools also play a vital role in
tackling with childhood obesity in that, schools that
promote physical activity and provide healthy meal
choices can encourage children to make healthier
lifestyle choices, hence greatly reduce obesity rates
(Veitch et al., 2011). Public spaces such as parks
with good accessibility and playgrounds with good
quality of recreational facilities are vital, for
maintaining a healthy weight for children since its
encouragement of children's proper levels of
physical activity (Van Lippevelde et al., 2012).
Besides, educational programs provided by the city
or local about healthy eating habits, can also
effectively prevent childhood obesity and help
families make informed choices about nutrition and
physical activity, leading to healthier lifestyles
(Story et al., 2009). NHS England (2023) 2022/23
school year report uncovered that, obesity rates were
higher among children from deprived areas, for
example, among Year 6 children 30.2% of children
in the most deprived areas were obese, compared
with 13.1% in the least deprived areas. These insights highlight the importance of national child and adolescent health programs in guiding public health policy and ensuring that resources are directed to the most affected communities. The programme's data will be crucial for developing effective health interventions and promoting healthy lifestyles among children in England.

## 2.3 Mental Health

On the other hand, mental health is another strand for Londoners' health issue. Chronic physical diseases increase as mental health levels decline (Yang et al., 2020). It can directly affect people's life experiences and quality of life in that, better mental health allows individuals to utilize their abilities and align with the broader values of society (Yang et al., 2006). However, it was not until the 1990s that people gradually realised the wider impacts of mental illness (Zhu et al., 2020). Mental health is defined as a state of well-being where individuals recognize their own abilities, can handle daily life stresses, works productively, and contributes to society (Daghagh et al., 2019). Mental health consists of the hedonic dimension, which focuses on well-being, fulfilment, and avoidance of pain, and the eudaimonic dimension, which includes personal growth, personal objectives, and mental functioning (Henderson and Knight, 2012; Ryan and Deci, 2001). The Public Health Agency of Canada (PHAC) defines mental health as the capabilities that every person possesses that enable them to experience emotions, think critically, and behave in ways that enhance their life enjoyment and their capacity to cope with challenges (Heather et al., 2017), especially in that, positive emotional and mental health that values the importance of culture, fairness, social equality, interconnectedness, and individual dignity (Heather et al., 2017).

To better measure citizens' mental health, Keyes (2002) proposed the "Mental Health Continuum" (MHC) model comprises of emotional well-being, psychological wellbeing, and social well-being. Headey et al. (1993) emphasized the dimensions on such as life satisfaction, positive emotions, anxiety and depression including factors such as socioeconomic status, social support, and life events, while the uneven distribution of these factors in different geographical areas may lead to significant differences in mental health status. Dfarhud (2014) reviewed that there are two dimensions which influence people's mental health, which are endogenic factors and exogenic factors. Endogenic

factors included the biological, cognitive, personality and ethical, which are challenging to be quantified, while exogenic factors, such as greeneries, are convenient factors to evaluate their influences on mental health. Markevych et al. (2017) suggested that the greenspace could enhance the interpersonal communication, relieve the pressure and protect the environment, all these advantages are generally related to the mental health. Vires et al. (2003) runs a test with 10,000 people and found that greener area benefits human's mental health, especially for the elderly and housewives. Gianfredi (2021) said that the publicly accessible greenspace in urban area has positive influence on both physical activity and mental health, and Lachowycz (2013) suggested that the greenspace area influences people potentially, even for those people do not walk in the greenspace they would feel more satisfied.

## 2.4 Spatial Analytics to Understand Urban Health

A comprehensive understanding of the various aspects of physical health and mental health can support better strategic policies on appropriate interventions to address areas where poor health is prevalent among London regions. In assistance of the development of geographical information system (GIS) and relevant analytical techniques, geospatial models and tools had been widely utilised in identifying the spatial patterns of obesity. For example, Sun et al. (2020) used spatial regression models, SAR and ESF, onto childhood obesity analysis and drew associations between childhood obesity prevalence and variables such as population density, race, unemployment rate, and household income. Cetateanu and Jones (2014) have used ESDA techniques to identify spatial clusters of obesity in England, on basis of Global Moran's *I* and Local Indicators of Spatial Association (LISA), they found important clusters of high rates of obesity in urban areas and low rates of obesity in rural areas, further highlighted the need for targeted public health interventions in specific regions (Anselin et al., 2006). In London, environmental factors such as socioeconomic deprivation and access to green space, has presented similar spatial clustering patterns (Gaber et al., 2024), which were suggested to be interlinked with the relationship fast-food restaurants and supermarkets on obesity rates (ESRI, 2021). Grigoroglou et al. (2019) illustrated that depression prevalence, to reflect mental health status, could be identified of their spatial clustering patterns through Moran's *I* measure.
Underweight men were found to have higher rates of anxiety disorders compared to normal-weight men and women (McLaren et al., 2008), and obese women have higher rates of mood disorders compared to normal-weight women. Daghagh Yazd et al. (2019) also agreed that female farmers suffer greater mental distress than male counterparts, and marital status is associated with poorer mental health (Daghagh et al., 2019). Houlden et al. (2019) examined the linear relationship between the amount of green space and mental health by developing a univariate ordinary least squares (OLS) regression model exploring relevant factors of mental health indices. The GWR method computes a local regression where the coefficients can vary spatially (Brunsdon et al., 1996; 1998), hence could provide a more nuanced understanding of how different factors influence mental health (SMI) in local areas, such as the work of Cruz et al. (2022) who constructed a Bayesian spatial regression model incorporating random effects applied to the log-transformed mean SMI prevalence, to offer a precise comprehending of the spatial pattern and determinants of mental health problems, in recognising that high risk of SMI was associated with unemployment, low income, low education level, and living in a high-crime area. Besides of the multifaceted nature of mental health linking to socioeconomic status, environmental factors such as greenery, air pollution, and climate also have been examined (Mueller et al., 2023; Houlden et al., 2019; Bakolis et al., 2021). The findings highlight the huge impact that climate and air quality have on the planet. In conclusion, these studies reveal the complex impact of environmental factors on obesity and mental health, highlighting the importance of further research and consideration of multiple variables.

# 3 DATA AND METHODOLOGY

## 3.1 Data

The datasets used in this study are derived from multiple open sources and integrated into the geographical boundaries for UK Census at Middle Layer Super Output Area (MSOA) and Lower Layer Super Output Area (LSOA) levels respectively. Data fusions are realised through joining into the boundaries' shapefiles by the shared MSOA or LSOA codes for following 4 datasets:

Firstly, measures for obesity and relevant socio-economic factors mainly come from MedSat data, under CC BY-SA 4.0 license and could be found at

TUMMedia (data sharing service from Technical University Munich) (Šćepanović et al., 2023), includes a wide array of variables (Figure 1) such as population density, age, gender, ethnicity, religion, marital status, employment, commute to work, residence, self-reported health, language, and the index of multiple deprivation (IMD), air quality, greenery, climate, and land cover, as well as medical prescriptions data encompasses conditions such as diabetes, hypertension, asthma, depression, anxiety, opioid prescriptions, and overall total prescriptions.

<figure><img src="image_12.png" /><figcaption>Figure 1: Structure of Medsat dataset (Access the dataset at: https://github.com/sanja7s/MedSat).</figcaption></figure>

The second dataset, Access to Healthy Assets and Hazards (AHAH), is collected from the Consumer Data Research Centre (CDRC), includes a variety of variables measuring access to both healthy assets and environmental hazards, such as the distance to the nearest gambling outlet, fast food outlet, GP practice, hospital, dentist, pharmacy, and leisure center, as well as measures of air quality (nitrogen dioxide, particulate matter) and green/blue space (NDVI value for passive green space), which were collocated from various national organizations such as ONS, NHS England, NHS Scotland, LDC, DEFRA, and OpenStreetMap (Figure 2).

The third dataset on adults and children obesity is sourced from the NHS Digital's Quality and Outcomes Framework (QOF), aggregated at the MSOA level (GLA, 2023). This dataset combines GP-level data with the 2019 population estimates from the Office for National Statistics to estimate the percentage and number of obese adults in each MSOA. Similarly, the childhood obesity dataset identifies MSOAs with the highest levels of overweight children in reception and Year 6, which are viewable as below interactively.
Component Variables

<table><caption>Figure 2: Detailed variables of CDRC dataset (Access the dataset at: https://data.cdrc.ac.uk/dataset/access-healthy-assets-hazards-ahah)</caption><thead><tr><th>Variable</th><th>Description</th><th>Domain</th><th>Country</th><th>Source</th><th>Date</th></tr></thead><tbody><tr><td>ls0e11</td><td>Lower Super Output Area code (2011)</td><td>NA</td><td>England & Wales</td><td>ONS</td><td>2011</td></tr><tr><td>ls0e11</td><td>Data Zones</td><td>NA</td><td>Scotland</td><td>gov scot</td><td>2011</td></tr><tr><td>ah3gemb</td><td>Distance to nearest Gambling Outlet (minutes)</td><td>retail</td><td>All</td><td>LDG</td><td>2019</td></tr><tr><td>ah3offord</td><td>Distance to nearest Fast Food Outlet (minutes)</td><td>retail</td><td>All</td><td>LDG</td><td>2019</td></tr><tr><td>ah3pous</td><td>Distance to nearest Pub/Bar/Nightclub (minutes)</td><td>retail</td><td>All</td><td>LDG</td><td>2019</td></tr><tr><td>ah3off</td><td>Distance to nearest Off licence (minutes) (REMOVED)</td><td>retail</td><td>All</td><td>LDG</td><td>2019</td></tr><tr><td>ah3tob</td><td>Distance to nearest Tobacco/ Vape Store (minutes)</td><td>retail</td><td>All</td><td>LDG</td><td>2019</td></tr><tr><td>ah3gp</td><td>Distance to nearest GP Practice (minutes)</td><td>health</td><td>England & Wales</td><td>NHS England</td><td>February 2022</td></tr><tr><td>ah3gp</td><td>Distance to nearest GP Practice (minutes)</td><td>health</td><td>Scotland</td><td>NHS Scotland</td><td>January 2022</td></tr><tr><td>ah3hosp</td><td>Distance to nearest Hospital (minutes)</td><td>health</td><td>England & Wales</td><td>NHS England</td><td>February 2022</td></tr><tr><td>ah3hosp</td><td>Distance to nearest Hospital (minutes)</td><td>health</td><td>Scotland</td><td>NHS Scotland</td><td>December 2021</td></tr><tr><td>ah3dent</td><td>Distance to nearest Dentist (minutes)</td><td>health</td><td>England & Wales</td><td>NHS England</td><td>January 2022</td></tr><tr><td>ah3dent</td><td>Distance to nearest Dentist (minutes)</td><td>health</td><td>Scotland</td><td>NHS Scotland</td><td>June 2021</td></tr><tr><td>ah3phar</td><td>Distance to nearest Pharmacy (minutes)</td><td>health</td><td>England</td><td>NHS England</td><td>January 2022</td></tr><tr><td>ah3phar</td><td>Distance to nearest Pharmacy (minutes)</td><td>health</td><td>Scotland</td><td>NHS Scotland</td><td>October 2021</td></tr><tr><td>ah3phar</td><td>Distance to nearest Pharmacy (minutes)</td><td>health</td><td>Wales</td><td>NHS Wales</td><td>November 2021</td></tr><tr><td>ah3les</td><td>Distance to nearest Leisure Centre (minutes)</td><td>health</td><td>All</td><td>LDG</td><td>2019</td></tr><tr><td>ah3blue</td><td>Distance to nearest Blue space (minutes)</td><td>green/bleuespace</td><td>All</td><td>OpenStreetMap</td><td>2021</td></tr><tr><td>ah3ples</td><td>NVDI value indicating Passive Green Space</td><td>green/bleuespace</td><td>All</td><td>Serentine Satellite</td><td>2021</td></tr><tr><td>ah3b2s</td><td>Annual mean Nitrogen Dioxide (µg/m³)</td><td>air</td><td>All</td><td>DEFRA</td><td>2019</td></tr><tr><td>ah3pm10</td><td>Annual mean Particulate Matter (µg/m³)</td><td>air</td><td>All</td><td>DEFRA</td><td>2019</td></tr><tr><td>ah3so2</td><td>Annual mean Sulphur Dioxide (µg/m³)</td><td>air</td><td>All</td><td>DEFRA</td><td>2019</td></tr></tbody></table>

The data for mental health modelling is Small Area Mental Health Index (SAMHI), which is a composite annual indicator of population mental health at LSOA level from 2011 to 2019 (Daras and Barr, 2020). SAMHI combined multiple sources of mental health data, such as NHS-Mental health related hospital attendances, GP Patient Survey, and Prescribing Data, where a positive SAMHI indicates relatively severer mental health issues in the area, whilst a negative SAMHI indicates for fewer symptoms of residents' mental health issues. On top of it, 11 independent variables had been selected for modelling, include household income, employment, physical health, education, environment, air pollution, green area, the percentage of the population over 65 years old, the percentage of men, population density, and the percentage of the population aged 16-29, derived from the English indices of deprivation (IMD) in 2015 and 2019 (ONS, 2024), and the total population, population density at LSOA scale, greenspace coverage, air pollution concentration data from London Data Store.

## 3.2 Research Design and Methods

The research workflow design diagram is identical for obesity and mental health, hence they are running in parallel following the same workflow shown in Figure 3, taking obesity as example for illustration. Geographic boundary files and datasets upon merging adult and childhood obesity rates were used to map obesity patterns in London, followed by appropriate exploratory spatial data analysis, spatial patterns recognitions (i.e., Global Moran's *I* and Local Moran's *I* clustering), and varied influential factors

investigation on obesity among adults and children, with step-by-step methods adoptions included in the lower half of Figure 3.3. The project employs spatial regression models, including SAR and SEM models, to identify the influential factors for whole city, but uses localised model Geographically Weight Regression (GWR) to find their varied influences among London areas.

Anselin et al. (2006)'s Global Moran's *I* and Local Moran's *I* indices were firstly calculated to examine the existence of spatial autocorrelations for health data, as well as the potential spatial clusters and outliners (Equation 1), so that to decide which regression models will be the optimal for this study.

$$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots + \beta_k x_k + u \quad (1)$$

Traditional regression model Ordinary Least Square (OLS) is going to be used for preliminary regression, as one of the most classical types of regression analysis (Yang et al., 2016), which predicts the output variable as a linear combination of the input variables; but with more recognition of the existence of spatial autocorrelation, spatial regression models are to be tested as well. For example, if taking the mental health measure SAMHI as dependent variable, then the model will be expressed as in Equation 2.

$$SAMHI = \beta_0 + \beta_1 \text{ income score} + \beta_2 \text{employment score} + \beta_3 \text{education score} + \beta_4 \text{health score} + \beta_5 \text{environment score} + \beta_6 \text{concentration} + \beta_7 \text{canopy tree} + \beta_8 \text{65aged} + \beta_9 \text{male ratio} + \beta_{10} \text{ density} + \beta_{11} \text{16 29ratio} + u \quad (2)$$

<figure><img src="image_13.png" /><figcaption>Figure 3: Research Design flowchart for Obesity (or Mental Health) Patterns in London</figcaption></figure>

Spatial Lag Model (SLM) is deployed to account for spatial dependencies by incorporating a spatial lag of the dependent variable into the regression model, embracing the influence of neighbouring areas on the
outcome of interest, and be expressed in Equation (3):

$$Y = \rho WY + X\beta + \epsilon \quad (3)$$

where Y is the dependent variable, ρ is the spatial autoregressive parameter, W is the spatial weights matrix, X represents the independent variables, β denotes the coefficients, and ε is the error term. By including ρWY.

Spatial Error Model (SEM) addresses spatial autocorrelation in the error terms rather than in the dependent variable. This model is suitable when the spatial dependence arises from unobserved factors that affect the error term, and can be formulated as in Equation (4):

$$Y = X\beta + \epsilon, \quad \epsilon = \lambda W\epsilon + \eta \quad (4)$$

where η is a normally distributed error term with mean zero and Wε represents the spatially lagged error term. Here, λ is the parameter that measures the degree of spatial autocorrelation in the errors. The SEM helps to correct for biases that might be introduced by spatial correlation in the error terms, leading to more accurate estimates of the relationships between the dependent variable and the independent variables.

Geographically Weighted Regression (GWR) as the last model, to capture local variations in the relationships between variables, offering a more detailed understanding of spatial dynamics. Unlike traditional regression models, which assume a uniform relationship across all locations, GWR calculates different regression coefficients for each area, addressing spatial differences in the data. The formula of the GWR model is displayed in Equation 5:

$$y_i = \beta_0(u_i, v_i) + \beta_1(u_i, v_i) x_{1i} + \beta_2(u_i, v_i) x_{2i} + \dots \\ \beta_k(u_i, v_i) x_{ki} + \epsilon_i \quad (5)$$

It detects spatial changes in relationships in the model and can reveal local features and trends in spatial data and handle spatial autocorrelation in data. Drill into the research design and methods for influential factor investigations, hierarchy of the selected factors are reflected in Figure 4, and similar for mental health measure (to replace the dependent variable from measure for obesity to measure for mental health), informed by empirical studies in Section 2.

<figure><img src="image_14.png" /><figcaption>Figure 4: Variables for Modelling Obesity Patterns in London (similarly for Mental Health).</figcaption></figure>

# 4 RESULTS

## 4.1 Spatial Patterns of Londoners Health

### 4.1.1 Physical Health – Obesity

Londoners' obesity patterns vary by age group in that, as reflected in Figure 5 on obesity rates among London regions, the redder in the map the higher values and vice versa. From left to right, maps are obesity patterns for adults, children in Reception (ages 4-5), and children in Year 6 (ages 10-11) respectively.

<figure><img src="image_15.png" /><figcaption>Figure 5: Londoners' Obesity Patterns by Group (Adult, Reception and Year 6).</figcaption></figure>

It is apparent that, areas in the outer East (coloured in dark red) have higher obesity rates such as boroughs Barking and Dagenham and Havering. Besides, there were more adults with obesity in the western borough Hillingdon as well; whilst obesity rates among younger children in reception (aged 4-5 years old) were more dispersedly distributed, with areas in central London, such as Newham and Tower Hamlets, and outer areas such as Brent in the North; obesity among bigger children in Year 6 (ages 10-11) were also high in areas such as Barking and Dagenham, Tower Hamlets, and Hackney, but with wider distribution than the younger counterpart.

To identify the potential spatial autocorrelation, Global Moran's I had been calculated for each of the three age groups' obesity rates, in return with
significant values as adult obesity at 0.6112, younger children at 0.2093 and older children at 0.2689. It is obvious that adults with obesity in London tend to cluster more in certain outskirt areas, whilst children with relatively much less pronounced clustering spatial pattern, but mostly in Northeast London. It implies that obesity issues among London adults may be more influenced by the neighbouring areas and local environment, hence leading to the localised spatial analysis visualised by LISA maps (Figure 6). In LISA maps, areas in red are HH clusters with high obesity rates and surrounded by similar neighbouring areas and predominantly located in the northeast parts of London regardless of age groups with consistently high obesity rates. On the contrary, areas in blue indicates LL clusters areas with low obesity rates surrounded by low obesity neighbours, indicating central and southwestern regions tend to be healthier physically. Meanwhile, areas with high obesity rates but-neighboured by low obesity areas are colored in orange, whilst light blue areas are the opposite. Adults and bigger children also experience higher obesity in some western boroughs, whilst younger children have higher obesity in central-west London for limited area.

<figure><img src="image_16.png" /><figcaption>Figure 6: LISA clusters among adult, reception and year 6 (left to right).</figcaption></figure>

## 4.1.2 Mental Health

Londoners' mental health indices distribution had been presented in Figure 7 for 2011 to 2019 respectively for comparison, but received similar spatial patterns. Redder areas with lower index values indicated better performance regarding residents' mental health, comparatively the bluer areas with positive mental health index values are those with worst performance of residents' mental health. From 2011 to 2019, residents' mental health has improved significantly with expanded redder areas.

<figure><img src="image_17.png" /><figcaption>Figure 7 Mental Health Indices Patterns (left: 2011; right: 2019).</figcaption></figure>

Upon calculating the global Moran's *I* measure, it was found that mental health index has significant spatial clustering patterns with a Moran's *I* value at 0.4875 in 2011 and 0.5341 in 2019. Localised LISA cluster mappings (Figure 8) further highlighted the clustering patterns with high-high (HH) areas indicating for those with worse mental health performance, and low-low (LL) areas with better mental health performance.

<figure><img src="image_18.png" /><figcaption>Figure 8 Mental Health Indices LISA maps (left: 2011; right: 2019).</figcaption></figure>

Boroughs such as Islington, Camden and Hackney kept poorer mental health over time, while boroughs such as Enfield, Sutton and Lewisham improved in 2019 and no longer being hot spot clusters. On the other hand, boroughs such as Brent, Hounslow, Redbridge, Newham, Harrow and Wandsworth were better performed over time, but Havering became getting worse in 2019 from its good performance in 2011.

## 4.2 Influential Factors from Regression Modelling

### 4.2.1 Influential Factors for Physical Health

To better investigate the contributing factors for London residents' obesity, OLS, SLM and SEM regressions had been conducted with results summarised in Table 1.

<table><caption>Table 1: Regression Results for Obesity Modelling.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">OLS</th><th colspan="2">SLM</th><th colspan="2">SEM</th></tr><tr><th>Adult</th><th>Bigger Children</th><th>Adult</th><th>Bigger Children</th><th>Adult</th><th>Bigger Children</th></tr></thead><tbody><tr><td>Intercept</td><td>14.7687</td><td>33.5218</td><td>2.2114</td><td>958.0314</td><td>---</td><td>1817.1489</td></tr><tr><td>Percent of Age 19~29</td><td>-0.0005</td><td></td><td>-0.0002</td><td></td><td>-0.0002</td><td></td></tr><tr><td>Percent of Age 40~49</td><td>---</td><td></td><td>---</td><td></td><td>---</td><td></td></tr><tr><td>Percent of Age 50~64</td><td>0.001</td><td></td><td>0.0007</td><td></td><td>0.0006</td><td></td></tr><tr><td>Percent of Age 85+</td><td>-0.0058</td><td></td><td>-0.0043</td><td></td><td>-0.0038</td><td></td></tr><tr><td>Distance to nearest Fast food outlet</td><td>---</td><td>-47.1416</td><td>---</td><td>38.4173</td><td>-0.0742</td><td>---</td></tr><tr><td>Distance to nearest Green Space</td><td>---</td><td>-1316.4992</td><td>0.8584</td><td>-656.2771</td><td>---</td><td>-879.9875</td></tr><tr><td>Net Annual Income</td><td>-0.0002</td><td>-0.0237</td><td>-0.0001</td><td>-0.0171</td><td>-0.0001</td><td>-0.0159</td></tr><tr><td>Percentage of unemployment</td><td>-3.3465</td><td>---</td><td>---</td><td>---</td><td>---</td><td>953.2364</td></tr><tr><td>Percentage of household deprivation</td><td>---</td><td>47910</td><td>41.7626</td><td>40950.0536</td><td>40.5875</td><td>43658.535</td></tr><tr><td>Neighbourhood spillover</td><td></td><td></td><td>0.9857</td><td></td><td></td><td></td></tr><tr><td>R-square</td><td>0.3346</td><td>0.3645</td><td>0.7662</td><td>0.5038</td><td>0.2356</td><td>0.3546</td></tr></tbody></table>
Comparing the 3 selected models, it is apparent that SLM is outperformed with R-square at 0.7662 for adults and 0.5038 for bigger children, indicating for the inclusion of neighbouring areas' influences (0.9857), especially for adults' obesity pattern. Income level exerted significant mediating effects onto obesity issue, regardless of age groups, but the influences were stronger to children than to adults. Percentage of residents at 40-49 years old is not related to regional adults' obesity level, but other age groups will be significantly related, especially the age group 50-59 years old, will drive mental health index higher. For children, community and family environment, such as the distance to the nearest fast-food outlet, the distance to the nearest green space, and the percentage of household deprivation had substantial effects on obesity rates, indicating that more convenient fast-food accessibility and more deprived families will drive children's obesity rate higher, while greenspace accessibility can mediate such obesity through easier exercise and outdoor activities.

## 4.2.2 Influential Factors for Mental Health

Similarly, London residents' mental health index and selected independent variables are modelled by OLS, SLM and SEM regressions for data in 2011 and in 2019 comparatively, with results summarised in Table 2.

<table><caption>Table 2: Regression Results for Mental Health Modelling.</caption><thead><tr><th rowspan="2"></th><th colspan="2">OLS</th><th colspan="2">SLM</th><th colspan="2">SEM</th></tr><tr><th>2011</th><th>2019</th><th>2011</th><th>2019</th><th>2011</th><th>2019</th></tr></thead><tbody><tr><td>R Squared</td><td>0.5983</td><td>0.5499</td><td>0.7238</td><td>0.7443</td><td>0.5390</td><td>0.5045</td></tr><tr><td>Income Score</td><td>-0.5020</td><td>-0.4295</td><td>-0.3528</td><td>-0.2147</td><td>-0.2098</td><td>-0.0605</td></tr><tr><td>Employment Score</td><td>0.8584</td><td>0.8837</td><td>0.6874</td><td>0.6272</td><td>0.5968</td><td>0.5301</td></tr><tr><td>Education Score</td><td>-0.1516</td><td>-0.1191</td><td>-0.1407</td><td>-0.1246</td><td>-0.0539</td><td>-0.0510</td></tr><tr><td>Health Score</td><td>0.5239</td><td>0.4034</td><td>0.3919</td><td>0.2683</td><td>0.5391</td><td>0.3746</td></tr><tr><td>Environment Score</td><td>0.0989</td><td>----</td><td>0.0501</td><td>-0.0306</td><td>0.0478</td><td>0.0115</td></tr><tr><td>Air Pollution Score</td><td>0.1139</td><td>----</td><td>0.0675</td><td>0.0396</td><td>----</td><td>----</td></tr><tr><td>Canopy - Tree</td><td>0.0407</td><td>0.0302</td><td>----</td><td>----</td><td>----</td><td>----</td></tr><tr><td>65aged</td><td>0.2513</td><td>0.2931</td><td>0.2124</td><td>0.2356</td><td>0.1467</td><td>0.2326</td></tr><tr><td>Male Ratio</td><td>-0.1106</td><td>-0.1173</td><td>-0.0527</td><td>-0.0358</td><td>----</td><td>----</td></tr><tr><td>Density</td><td>----</td><td>----</td><td>-0.0330</td><td>----</td><td>----</td><td>-0.0333</td></tr><tr><td>16-29ratio</td><td>-0.0607</td><td>0.0114</td><td>-0.0546</td><td>----</td><td>-0.050</td><td>-0.0400</td></tr><tr><td>Spatial Weight</td><td>----</td><td>----</td><td>0.5020</td><td>0.5924</td><td>0.7978</td><td>0.8145</td></tr></tbody></table>

It is obvious that SLM model still have the highest R squared value for both 2011 and 2019 data, hence should be the optimal model to be selected. Residents' mental health was mostly driven by their deprivations of employment and health, but can be mediated if be with a lower deprivation of income. Similarly, deprivation on education and the percentage of male residents could also help to alleviate local mental health issues. However, the neighbouring areas' mental health level is influential more and more greatly over time, as well as the

percentage of aged populations (over 65). It also called our attention that at global scale, greenery didn't play significant influence onto Londoners' mental health, but air pollution concentrations are.

## 4.2.3 Localised Influences - GWR Model

To better understand the varied influences and model fitting among London boroughs, Geographically Weighted Regression (GWR) model had been applied to map out the factors' explanation ability for obesity and mental health measures. GWR allows for the examination of local variations in predictors, in Figure 9 it provides a deeper understanding of factors influencing obesity among London regions, by visualizing how well the model explains obesity rates across the study area.

<figure><img src="image_19.png" /><figcaption>Figure 9: Local R-Squared map of GWR model among adults and children.</figcaption></figure>

The local R-squared values for adults (left in Figure 9) are higher in East London especially the southeast such as Bromley, suggesting that the model explains obesity rates better in these regions. In contrast, the western areas show lower R-squared values, indicating that the model's explanatory power is weaker. For children (right in Figure 4.5), the local R-squared values are higher in the South and some northern areas, implying that the model performs better in explaining childhood obesity rates in these regions, but is weaker in explaining young people's obesity in central, and central east London areas.

It can also exhibit the varied influences onto dependant variable among London regions for each identified factor. For example, in Figure 10 presented the deprivation of income in London exerted varied influences onto mental health levels in 2011 and 2019, where outskirt areas were more driven by such economic inequality; on the other hand, it tends to cluster towards certain areas such as in Bromley over time.

Another factor, deprivation on employment has been investigated as well (Figure 11) in finding its expanded influences in the South and West London from 2011 to 2019, where central London and South London such as Croydon were always the most-hit areas for employment inequality driven mental health issues.
<figure><img src="image_20.png" /><figcaption>Figure 10: Local Influences on Mental Health from Deprivation of Income (left: 2011; right: 2019).</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Figure 11: Local Influences on Mental Health from Deprivation of Employment (left: 2011; right: 2019).</figcaption></figure>

In all, each factor can be mapped across London areas on their varied influences onto local mental health levels, either be driving forces or mediating effects. From a global perspective, greenery is not significantly influencing mental health, but it became influential locally in certain boroughs such as Hounslow. Income, education, employment, physical health, environment, greening, air pollution concentration, population density, and the percentage of young and elderly populations are important factors affecting mental health. These findings are conducive to designing localised intervention measures according to the featured circumstances of varied boroughs.

# 5 CONCLUSIONS

Obesity is a significant public physical health issue, leading to health risks, including developing chronic conditions such as heart disease, type 2 diabetes, and certain types of cancer. To understand and mitigate its potential impacts on Londoners' health could be meaningful to the society's development. The findings highlighted significant factors influencing obesity, differing between adults and children, underscoring the need for targeted, location-specific public health interventions. Specifically, for adults, age group compositions, green space access, household deprivation, and income were significant; while for children, access to fast food outlets, green space, household deprivation, and income levels were critical. Recommendations included promoting weight management programs at local community, improving green space access, and addressing socio-

economic disparities. The GWR model highlights the significance of contextual and geographical factors in obesity. By focusing on local variations, more effective policies can be developed. For instance, in eastern boroughs like Newham and Tower Hamlets, strategies could include enhancing green spaces and providing financial support to low-income households. In central areas such as Islington and Camden, where childhood obesity is a major concern, interventions might include reducing fast food outlets near schools, expanding recreational facilities, and promoting nutrition education. Tailoring these strategies to regional needs can significantly improve public health outcomes and address obesity more effectively.

Mental health issue is not a sole challenge in London, instead serious mental illness (SMI) affected millions of people all over the world (James et al., 2018). In England, nearly three million adults were listed on local GP registers with depression in 2013-2014, and around half a million were diagnosed with a serious mental illness (SMI), which refers to people with schizophrenia, bipolar disorder or other psychotic illnesses (Grigoroglou et al., 2020). In London it is especially crucial to understand the mental health status over areas and time. The spatial patterns of London's mental health indices from 2011 to 2019 were largely consistent but varied at finer granularity for local distribution. Comparatively, the overall mental health indices in 2019 have improved significantly. Some boroughs, such as Islington, Camden and Hackey, kept poorer mental health performance from 2011 to 2019, whilst some other borough (Enfield, Sutton and Lewisham) exhibited great improvement over the time. On the contrary, outskirt boroughs such as Brent, Hounslow, Redbridge, Newham, Harrow and Wandsworth maintained good mental health performance over this period, but with exceptional deterioration in Havering from 2011 to 2019.

The spatial lag models (SLM) were found to be optimal in capturing maximum information hence explaining the dependent variables. The regional deprivations on income, employment, education, physical health, environment, air pollution, the proportion of the young and elderly population, population density, and the proportion of males significantly impacted mental health regardless of the year. Geographically, Hounslow, Redbridge and Harrow are boroughs consistently with good mental health, where Hounslow benefits from good employment equality, Redbridge and Harrow both benefit from better environmental equity, as well as better education in the latter. However, Havering
stood out as an example for mental health deterioration due to severe income deprivation and air pollution pressures. Environmental factor greenery didn't exert a significant impact on London's mental health, but significantly affected mental health locally in specific boroughs. For example, Havering's mental health deteriorated significantly from 2011 to 2019 when driven by widened income gaps, high level of air pollution and less access to greeneries.

Mental health is affected by a mixture of multiple socioeconomic and environmental factors, future trajectory studies could be suggested to investigate the long-term impacts. Qualitative research can also be recommended to complement the findings interpretation from residents' point of view by interviews. The impact of each variable on mental health varies significantly across regions and years, reflecting the spatial heterogeneity of different regions. For example, Greening can mediate the mental health in Hounslow, but deprivation of employment could drive mental health level up. On the other hand, some factors only were influential in the area for certain time, such as deprivation on income and education were only negatively related with mental health in 2011, but no longer being significantly influential in 2019. Such variations might be the results from local policy interventions hence being worthwhile to get evaluated.

The project successfully addresses several limitations from empirical studies in that, it fills the gaps by simultaneously analyzing both adults and childhood obesity, offering a more comprehensive understanding of obesity across different age groups; compared the trajectory changes of mental health spatial patterns from 2011 to 2019 over both space and time, and highlights the necessity of region-specific policies through various spatial regression methods, demonstrating the importance of considering local context in public health interventions for both physical and mental health. Future research could benefit from integrating socio economic and environmental factors influencing obesity and mental health, such as air quality, dietary habits, and physical activity levels. Expanding the study to include longitudinal data or qualitative research data could provide insights into causal relationships. Incorporating a spatial-temporal analysis would allow for examining how childhood obesity transitions into adult obesity, mental health had been influenced by neighbouring areas, and revealing long-term trends and patterns. This approach would involve tracking cohorts of individuals over time to observe how health related challenges develop. By linking multi-sourced data,

researchers can identify critical periods and factors that influence the health trajectories, providing a deeper understanding of how society conditions affect long-term health outcomes for citizens.

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# Navigating Points of Interest: The Dog-Walker Pathfinding Algorithm

Natsuki Tsutsui<sup>a</sup> and Shohei Yokoyama<sup>b</sup>

Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan
tsutsui-natsuki@ed.tmu.ac.jp, shohei@tmu.ac.jp

**Keywords:** Geographic Information System, Pathfinding Algorithm, Flickr.

**Abstract:** Navigating complex environments that encompass both road networks and points of interest (POIs) demands innovative pathfinding solutions. Traditional algorithms, such as Dijkstra's, primarily focus on finding the shortest path between nodes on a graph and are not designed to handle the additional complexity of scattered POIs. This paper introduces the dog-walker pathfinding algorithm, a novel approach that integrates the discovery of waypoints and greedy routing into a single process. By simulating the dynamics between two agents—a dog motivated by POIs and a walker navigating a graph—the algorithm dynamically identifies routes that connect key locations and pass through areas rich in POIs. This method leverages road networks and POIs to provide routes from the start point to the end point tailored to user preferences. Owing to the minimal data requirements for POIs, this method can be easily integrated with various data sources, including X (formerly Twitter), Google Maps, and Instagram. In this study, we develop an agent-based online algorithm inspired by the dogwalker. We verified the algorithm using POI data obtained from Flickr and Google Maps, demonstrating its application in real-world scenarios such as recommending tourist routes. Demonstrations show that our algorithm effectively generates routes that align with user preferences, as evidenced by qualitative and quantitative assessments. Additionally, we demonstrated that our method operates more rapidly than methods utilizing Dijkstra's algorithm.

## 1 INTRODUCTION

Pathfinding algorithms are essential for numerous applications related to real-world mobility. They are widely used for finding the shortest route between two points in large graph data, such as road maps. Additionally, these algorithms help manage complex queries such as route optimization, automated driving, and tourist route recommendations.

In this paper, we introduce the dog-walker algorithm, a novel method designed to find routes that cater to user interests within large graph data spaces with numerous point of interest (POIs).

The proposed method utilizes data from Open Street Map<fnref n="1" /> and Flickr<fnref n="2" />. Figure 1 illustrates the targeted spatial area. By integrating geographical information and social media content, this approach enriches the analysis and enhances the precision of the methodological application. In our graph, road network edges are depicted by blue lines, while POIs are

represented by red dots. The algorithm identifies efficient routes in such spaces, favoring regions with a high density of POIs.

Traditionally, algorithms such as Dijkstra's are used to find optimal routes on graphs. However, these methods are not equipped to handle spaces where both graphs and POIs coexist, as targeted in this study. In typical road networks, nodes are placed at intersections, but our usual start and end points, such as buildings or parking lots, are not directly on the road. Similarly, most landmarks do not reside directly on the road.

This concept is particularly suited for applications using geosocial big data. For instance, consider the graph as a road network and the POIs as geotagged photos shared on social media sites. A geotag represents the latitude and longitude coordinates where the photo was taken, provided by the GPS of a smartphone or other device. Locations with a high density of geotags are noteworthy. Geotags often exist independently from the road network and may have a fuzzy density distribution due to GPS errors.

The objective of the proposed method is to discover routes on the road network that pass through

<footnote>a https://orcid.org/0009-0000-2390-5572</footnote>

<footnote>b https://orcid.org/0000-0002-0550-617X</footnote>

<footnote n="1">¹https://www.openstreetmap.org/copyright/en</footnote>

<footnote n="2">²https://www.flickr.com</footnote>
<figure><img src="image_22.png" /><figcaption>Figure 1: OpenStreetMap data in Kyoto Prefecture, Japan. Blue lines represent road network edges and red dots represent POIs.</figcaption></figure>

locations with a high density of POIs that are not di-
rectly on the road network but exist in the same space.

This algorithm can be applied to recommend sightseeing routes based on geosocial data. Conventionally, recommending tourist routes required two steps: recommending waypoints and discovering routes connecting them. For example, POIs are density-clustered by DBSCAN, and routes are discovered using nodes in the cluster as waypoints. In contrast, the proposed method is an online algorithm that simultaneously discovers waypoints and optimal routes.

The dog-walker pathfinding algorithm works on the principle of two agents: a dog and a walker. The walker navigates the graph, while the dog moves independently within a two-dimensional space, motivated by POIs. The dog and walker are linked by a lead, with the walker propelled in the direction the dog wishes to go. The dog, motivated by POIs acting as food, moves toward areas where POIs are abundant. Once the dog has consumed enough POIs, it gravitates toward its goal. The path that the walker takes, led by the dog, is the output of this algorithm, anticipated to pass through areas with a high density of POIs.

The contributions of this study are as follows.

1. Identifying waypoints based on POIs.

2. Discovering efficient routes around waypoints.

3. Developing an algorithm that combines the above two steps.

4. Creating agent-based online algorithms inspired by the dog-walker concept.

5. Demonstrating a strong relevance to real-world applications.

In this study, we qualitatively and quantitatively evaluated the recommended routes assuming user preferences using geosocial data, and the results indicated that route recommendations were effectively made. Additionally, we demonstrated that our method operates more rapidly than methods utilizing Dijkstra's algorithm.

The remainder of this paper is structured as follows: Section 2 covers related work, Section 3 details our proposed method, Section 4 presents the experiments, and Section 5 concludes the paper and outlines future work.

## 2 RELATED WORK

Research utilizing geotagged social data has explored not only route recommendations based on user preferences but also methods that consider various factors such as time, distance, route safety, weather, and crowding as costs in the calculation. Additionally, studies have focused on identifying popular tourist spots using geotagged social data.

Yang et al. (Yang et al., 2011) proposed a versatile and highly accurate method for identifying tourist spots using geotagged social data collected
from Flickr in Paris, Hong Kong, and New York. This method employs self-tuning spectral lustering, which does not require parameter settings, allowing for consistent clustering applicable to cities and locations with different POI characteristics.

Chen et al. (Chen et al., 2011) proposed a personal trajectory prediction system based on GPS data, using a new algorithm called Continuous Route Pattern Mining to extract route patterns. This system predicts future routes by utilizing route patterns extracted from an individual's past trajectory data. Popescu et al. (Popescu and Grefenstette, 2009) developed a method to estimate visit times and daily tourist routes using geotagged social data from Flickr in London, New York, Paris, and San Francisco. The proposed method automatically estimates visit times to tourist locations by utilizing the timestamps and location information of photos posted by users. Kurashima et al. (Kurashima et al., 2010) proposed a system that models the history of places visited by tourists using geotagged social data from Flickr in New York and San Francisco. This system recommends travel routes based on the user's current location and individual interests, leveraging data from large-scale photo-sharing sites for route recommendations, unlike previous studies that utilized GPS data. Ishizaki et al. (Ishizaki et al., 2021) introduced an algorithm called P-UCT that recommends routes best matching user preferences. The method includes generating evaluators using Support Vector Machine and route generation using Monte-Carlo Tree Search. Jie et al. (Bao et al., 2012) developed a system that automatically learns user preferences from location histories and extracts local expert opinions to provide recommendations. This system collects data collected from Foursquare in New York and Los Angeles. All of these route recommendation and estimation methods require user movement records and specific data sources, leading to challenges in data acquisition and the insufficiency of necessary data in certain regions.

De et al. (De Choudhury et al., 2010) proposed a method for generating tourist routes using geotagged social data from Flickr. This method uses geographic and temporal information to create timed paths based on each user's visited locations, stay durations, and travel times. By applying an orienteering problem algorithm (Tsiligirides, 1984), the system recommends optimal travel routes. Furthermore, Majid et al. (Majid et al., 2015) introduced a method for recommending suitable tourist destinations and travel routes using geotagged social data from Flickr, tourist data from Google Places, and historical weather data. These studies differ from our research in that they require pre-existing information about tourist spots for

route recommendations. Jain et al. (Jain et al., 2010) proposed a system called Antourage to recommend tourist routes using geotagged social data from Flickr. In this method, users specify a starting point and a maximum travel distance, and the system suggests routes that visit popular tourist destinations within those constraints. The route exploration algorithm employs the Max-Min Ant System, a metaheuristic algorithm developed based on the behavior of real ants, as proposed by Dorigo et al. (Dorigo et al., 1996) (Dorigo and Gambardella, 1997) (Dorigo and Di Caro, 1999). It is primarily applied to combinatorial optimization problems and is particularly effective for the Traveling Salesman Problem (TSP). This algorithm consists of pheromone trails, pheromone updates, probabilistic path exploration, and an iterative process. Antourage recommends tours that visit popular tourist destinations; however, its optimization algorithm is time-consuming, making it unsuitable for online processing.

Kim et al. (Kim et al., 2014) proposed a navigation system called SocRoutes, which uses crime history data from Chicago and geotagged social data from X to suggest routes based on the regional context, particularly emotions. Unlike traditional navigation systems that suggest routes based on the shortest distance or fastest time, SocRoutes considers emotional context when suggesting routes. Fu et al. (Fu et al., 2014) introduced TREADS, a travel route recommendation system that leverages social media data, specifically X and Yelp reviews, to recommend safe and interesting travel routes in real time. TREADS takes into account the user's interests and safety, employing text summarization techniques to provide summaries of X data and Yelp reviews related to the locations on the recommended route. Quercia et al. (Quercia et al., 2014) developed a system that recommends not only the shortest route in urban areas but also emotionally pleasant routes. They retrieved the top k-shortest paths and evaluated these routes using geotagged data based on criteria such as "beautiful," "quiet," and "happy." These route exploration methods are designed for specific purposes, unlike our research, which aims to integrate multiple user preferences and interests.

Yamashita et al. (Yamashita and Yokoyama, 2022) proposed a method for recommending routes based on user preferences by dividing a map into a grid and assigning weights to the edges of the graph based on nighttime light data and geotagged tweets from X. They applied Dijkstra's algorithm to find optimal routes. While this method shares similarities with our research in terms of integrating various data sources and recommending routes based on user pref-
ferences, it relies on Dijkstra's algorithm (Dijkstra, 1958), which tends to avoid recommending routes that significantly deviate from the shortest path. In contrast, our study allows for adjusting the extent to which POIs are collected based on parameters, thus recommending more interesting detours.

# 3 PROPOSED METHOD

This section details the proposed method. Figure 2 provides an overview of our methodology, which is divided into three phases: dataset, preprocessing, and main processing.

The dataset phase consists of POI data (Section 3.1) and road network data (Section 3.2). During preprocessing, we extract specific POIs from the POI data (Section 3.3) and remove dead-end nodes from the road network (Section 3.4), overlaying them onto the same plane. We then assign POIs to each node (Section 3.5) and precompute the interest vectors (Section 3.6). In the main processing phase, we set start and end points on the preprocessed data, apply Dijkstra's algorithm (Section 3.7) to construct the shortest path tree, and execute the dog-walker pathfinding algorithm (Section 3.8).

Additionally, “POIs-on-map” refers to the situation where POIs overlap on the road network, while “Weighted POIs-on-map” indicates a state where data has been preprocessed, with POIs assigned to each node.

## 3.1 Data on POIs

We use geotagged POI data for route searching. The POI data requires only coordinate information on a two-dimensional plane (latitude and longitude), making it applicable to various data sources.

We utilize geotagged data shared on social networking services as POI data. Geotags represent the coordinates where the data were posted, suggesting that areas with high geotag density may contain points of interest. Therefore, our method suggests a route that includes waypoints densely populated with geotags en route to the destination. We have employed data from sources such as Flickr and Google Maps<fnref n="3" />; however, our methodology is adaptable to various sources, including Instagram <fnref n="4" /> and X <fnref n="5" />, allowing for the integration of different data sources.

## 3.2 Road Network

We define the road network as a simple undirected graph with non-negative weights, excluding one-way streets. The graph is denoted as $G = (V, E)$, where $V$ represents the set of nodes, and $E$ represents the edges of the road network. Each edge is represented as an unordered pair of vertices, $u, v \in V$, where $(u, v) \in E$ and $u \neq v$.

The cost between nodes is represented as $w(u, v)$, where $w(u, v) = w(v, u)$ and $w(u, v) > 0$. Each node has associated two-dimensional coordinate information; if the coordinates of nodes $u$ and $v$ are $x_u$ and $x_v$, respectively, then $x_u \neq x_v$ when $u \neq v$.

Furthermore, we denote the straight-line distance between coordinates $x_u$ and $x_v$ as $l(x_u, x_v)$. However, $l(x_u, x_v)$ does not necessarily equal $w(u, v)$ because real-world roads are not composed of straight lines.

## 3.3 Extraction POIs

The extracted POIs contain various metadata depending on the dataset. For instance, Flickr provides metadata such as latitude and longitude, images, creation time, text, and tags. Google Maps might include reviews and store names. These metadata are used to extract subsets according to user preferences. For instance, data extracted from Flickr using the text “temple,” can be used by tourists interested in visiting temples.

In this study, we focus on the dog-walker pathfinding algorithm and do not discuss the method of extracting data based on user preferences. However, our method assumes that the POIs, based on user preferences, only require latitude and longitude, making it compatible with various datasets.

## 3.4 Removal of Dead-End Nodes

The proposed method relies on the dog agent moving toward POIs, which makes it vulnerable at dead-end nodes. This occurs because the dog agent is drawn to the POIs regardless of dead ends, leading to a loop when a waypoint exists beyond the dead-end node. To ensure efficient navigation, a preprocessing step is necessary to remove these nodes. Therefore, we removed the dead-end nodes using the existing method by Jiyoung et al (Pung et al., 2022).

We remove edges connected to nodes of degree one, ensuring that each node $v$ in the graph $G$ satisfies $\deg(v) \ge 2$.

Additionally, road networks often represent the same street with two edges separated by lanes, such as in multilane roads. Thus, merely deleting nodes

<footnote n="3">³https://www.google.com/maps</footnote>

<footnote n="4">⁴https://www.instagram.com</footnote>

<footnote n="5">⁵https://www.x.com</footnote>
<figure><img src="image_23.png" /><figcaption>Figure 2: Overview of proposed method.</figcaption></figure>

of degree one does not completely remove dead-end nodes. However, this issue is peripheral to our core problem and will not be further discussed here.

The preprocessed road network graph and the extracted POIs are combined on the same plane as POIs-on-map, which is then passed on to the subsequent processing.

## 3.5 Assigning POIs

For each node, we record a set of POIs within a distance $L[m]$ (in m). To calculate the distance between a node and a POI, we use the Haversine formula, which treats the Earth as a perfect sphere using spherical trigonometry. If the longitude and latitude of point A are $(\phi_a, \psi_a)$ and those of point B are $(\phi_b, \psi_b)$, the distance between points A and B is given by the following formula:

$$L = R \cos^{-1} (\sin \psi_a \sin \psi_b + \cos \psi_a \cos \psi_b \cos \Delta\phi) \quad (1)$$

As shown in Figure 3, when an agent reaches a node, the POIs recorded at that node are considered to be collected by the dog agent. The nodes that have been collected in this manner no longer influence subsequent route searching. This process is detailed further in Section 3.8, “Execution of the Dog-Walker Pathfinding Algorithm.”

## 3.6 Calculation of Interest Vectors

For each node, the sum of the dog's interest vectors for all POIs is calculated using the following formula:

$$d(\mathbf{x}_i, \mathbf{x}_p) = \frac{1}{|\mathbf{x}_p - \mathbf{x}_i|^n} (\mathbf{x}_p - \mathbf{x}_i) \quad (2)$$

$$I_i^D = \sum_{p \in \mathbf{P}} d(\mathbf{x}_i, \mathbf{x}_p) \quad (3)$$

<figure><img src="image_24.png" /><figcaption>Figure 3: Collection method of POI by the dog agent in the proposed route recommendation system. The walker agent (depicted as a human silhouette) remains at the reached node, while the dog agent (depicted as a dog silhouette) collects POI (red circles) within a defined radius (black circle) around the node.</figcaption></figure>

**Description 1.** Blue lines represent road network edges and red points represent POI.

Here, $\mathbf{P}$ represents the set of POIs, $\mathbf{x}_p$ is the position vector of a POI, $\mathbf{x}_i$ is the position vector of a node, and $n$ is a parameter that determines the weight given to distance. A larger value of $n$ indicates a greater interest in nearby POIs. The dog agent determines its next node based on this interest vector $I_i^D$.

## 3.7 Application of Dijkstra's Algorithm

When a user specifies the start and end points in latitude and longitude, the nearest nodes are identified and assigned as the respective start and end points. Subsequently, Dijkstra's algorithm is applied starting from the end node to construct the shortest path tree, recording the parent of each node in the process. This
information enables the walker agent to navigate to-
ward the end node.

This process is initiated after the user provides the
start and end points, thus constituting a part of the on-
line processing phase. As such, Dijkstra's algorithm
incurs a computational cost of O(N log M), where N
represents the number of nodes and M the number of
edges.

## 3.8 Execution of the Dog-Walker Pathfinding Algorithm

This section describes the dog-walker pathfinding al-
gorithm. In the proposed method, the agent begins at
the start node and follows these steps until it reaches
the end node: (1) retrieve POIs, (2) recalculate the
interest vector, and (3) determine the next node.

### 3.8.1 Retrieval of POIs

The dog agent collects POIs within a radius of $L[m]$ from the reached node and stores them in a set. Once collected, these POIs no longer influence any node. The targeted POIs are assigned to nodes during preprocessing, and except when $L$ is excessively large, the overall computational complexity remains within approximately $O(|P|)$. ($|P|$ is the number of POIs)

As the collected POIs no longer have any influ-
ence, it is necessary to recalculate the interest level
when deciding the next node. The method for recal-
culating this interest level is detailed in the following
section.

### 3.8.2 Recalculate the Interest Vector

When the agent decides the next node, it calculates
the interest vector $I_i$ following the formula:

$$I_i = C I_i^D + I_i^W \quad (4)$$

Here, $I_i^D$ represents the dog's interest vector for POIs at the current node that have not yet been collected, while $I_i^W$ represents the walker's interest vector. Therefore, the interest vector $I_i$ for the agent is determined by the sum of the interest vectors of both the dog and the walker. Additionally, $C$ is a parameter that dictates whether the dog or walker has a greater influence on decision-making.

As the movement of the node is determined by the
direction of the vector $I_i$, scalar values are essentially
meaningless in this context. Therefore, the parameter
is set for only one of the terms.

$$I_i^D = \begin{cases} \sum_{p \in P} d(\mathbf{x}_i, \mathbf{x}_p) - \sum_{q \in Q} d(\mathbf{x}_i, \mathbf{x}_q) \\ \sum_{p \in P \setminus Q} d(\mathbf{x}_i, \mathbf{x}_p) \end{cases} \quad (5)$$

Here, $Q$ is the set of collected POIs. If the set of
collected POIs is less than half of the total POIs, the
calculation is done by subtracting from the prepro-
cessed interest vectors. If more than half, the interest
vectors of the uncollected POIs are added to find the
dog's interest vector. This yields the same result with
either formula, regardless of the number of collected
POIs. However, for a large number of POIs, or when
few POIs are collected, this can significantly reduce
execution time.

Additionally, $I_i^W$ is calculated according to the fol-
lowing formula:

$$I_i^W = \frac{L_{now}}{L_{min}} d(\mathbf{x}_i, \mathbf{x}_j) \quad (6)$$

Here, $j$ denotes the parent node of $i$, and $\mathbf{x}_j$ and $\mathbf{x}_i$ represent the coordinates of these nodes, respectively. $L_{min}$ is the shortest path length from the origin to the destination calculated a priori using Dijkstra's algorithm, while $L_{now}$ denotes the cumulative distance traveled by the agent from the start node to its current node. Consequently, as the path length increases, the walker agent experiences a stronger compulsion toward the end node.

### 3.8.3 Determining the Next Node

The next node is determined by selecting the node that
has the highest cosine similarity between the direction
vector from the current node to an adjacent node and
the interest vector.

$$k = \underset{j}{\operatorname{argmax}} \frac{\mathbf{I}_i \cdot \mathbf{v}_i, j}{|\mathbf{I}_i| |\mathbf{v}_{i,j}|} \quad (i, j \in \mathbf{E}) \quad (7)$$

Here, $\mathbf{v}_{i,j}$ represents the direction vector from node $i$ to node $j$. If the next node is not the end node, the agent repeats this process starting from the POI collection phase.

Our method requires calculating the influence
from each POI as the agent moves between nodes,
which can be time-consuming for route search. The
worst-case computational complexity of the route
search is the product of the number of nodes tra-
versed and the total number of POIs used. There-
fore, the computational complexity increases propor-
tionally with the number of POIs. However, as the
POIs collected by the agent do not affect subsequent
route searches, the number of POIs that influence the
search decreases as the search progresses. Moreover,
if the influence of all POIs and the set of nearby POIs
is precomputed for each node during preprocessing,
the actual execution time becomes significantly faster
than the theoretical computational complexity.
# 4 EXPERIMENTS

In this section, we describe the datasets used, and present the results and analyses of the experiments.

## 4.1 Datasets

### 4.1.1 Flickr

Flickr is a photo-sharing platform that allows users to upload, share, and tag photographs and videos. It features an extensive database of billions of images, many of which are geotagged. This geotagged data have been utilized in a wide range of fields, including geographic information system (GIS) research, computer vision, and social sciences for analysis and visualization purposes.

However, our proposed method does not rely on user movement histories or images, which enhances its compatibility with data sources other than Flickr. In this study, Flickr was chosen because of the ease of data acquisition and the large scale of its data sources.

### 4.1.2 Google Maps

Google Maps is a Web-based mapping service that provides satellite imagery, street maps, panoramic street views (street views), real-time traffic conditions (Google Traffic), and route planning for walking, driving, cycling, and public transit. As a rich source of geographic data, Google Maps been extensively used in various fields, including urban planning, transportation, and tourism. Detailed and frequently updated data aid precise planning and analysis of spatial patterns.

In our research, Google Maps was selected as the second source of POIs data because of the ease of data acquisition and availability of additional information such as reviews.

### 4.1.3 Open Street Map

Open Street Map is a free user-generated database of map data that offers a comprehensive array of geographic information, including roads, buildings, and terrain, from around the world. This platform is an open-source project that allows map data to be freely used for both commercial and non-commercial purposes.

Open Street Map data are utilized in a variety of applications such as location-based services, navigation tools, urban planning, and transportation system research.

## 4.2 Experimental Setting

For our experiments, we used Flickr data spanning three years, from January 1, 2021 to January 1, 2024. Data for specific segments in Times Square, Manhattan, New York, USA, and Kyoto City, Kyoto Prefecture, Japan were gathered from Flickr, Google Maps, and OpenStreetMap. These datasets includes POIs and road network data.

From Flickr, we extracted posts within the targeted segments that included relevant keywords in their descriptions or tags. Data extraction from Google Maps was conducted manually, selecting appropriate content within the targeted segments. The acquired road network data consisted of walker pathways within the designated segments.

In each experiment, we collected POIs and road network data within specified geographical boundaries. For Times Square, the collection was within the latitude range of 40.745149 to 40.767065 and longitude range of -73.996706 to -73.970711. Similarly, in Kyoto, the data were gathered within latitude 34.99054 to 34.99940 and longitude 135.76802 to 135.78408.

The acquired road network data consisted of walker pathways within the designated segments.

## 4.3 Experimental Result and Analysis

### 4.3.1 Experiments with Flickr Data

We conducted experiments using data from Flickr on Times Square, Manhattan, New York to validate our proposed method. Figure 4 (a) illustrates the results of a route search from latitude 40.74510, longitude -73.99670 to latitude 40.76710, longitude -73.97080, restricted to posts containing the text 'cityscape.' The blue route represents the shortest path, while the orange route depicts the path determined by our proposed method. Figure 4 (b) illustrates the results of a route search restricted to posts containing the text 'art' in the same section.

Figure 5 shows the waypoints traversed by the route determined by our proposed method, as illustrated in Figure 4. The first waypoint is along the 7th Avenue, a well-known and bustling area in Times Square. The next waypoint spans horizontally at the Top of the Rock observation deck, where most posts focus on the night view. Furthermore, for routes tagged with 'art', after navigating several smaller waypoints, the route notably passes through a significant waypoint at The Museum of Modern Art (MoMA).

These figures demonstrate that the route generated by our proposed method effectively passes through
<figure><img src="image_25.png" /><figcaption>Figure 4: Results of the experiment that used the proposed method with Flickr data in parts of Manhattan, New York, USA. The gray, blue, and orange lines represent the road network, shortest route, and recommended route derived using the proposed method. Red dots represent POIs.</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>Figure 5: Details of waypoints passed by the proposed method in Times Square.</figcaption></figure>

waypoints associated with the text 'cityscape' and
'art' within the area.

**4.3.2 Experiments with Google Maps and Flickr Data**

In Kyoto City, Kyoto Prefecture, we conducted ex-
periments using data from Google Maps and Flickr
to validate our proposed method. Figure 6 visu-
alizes POIs related to ‘temple’ buildings along the
popular tourist route from Kiyomizu-Gojo Station to
Kiyomizu-dera Temple. In the experiments using
Google Maps data, temples within the segment were
designated as POIs, which are listed in Table 1.

Posts containing the keyword 'temple' within the
studied segment were extracted from the Flickr data.

In each figure, the blue and orange routes depict the
shortest and recommended paths, respectively.

Figure 7 details the waypoints passed by the route
recommended by our proposed method shown in Fig-
ure 6. The first encountered waypoint is the Yasaka
Pagoda at Hokan-ji Temple, a renowned photographic
landmark. The next waypoint is Kiyomizu-zaka, a
major street leading to the Kiyomizu-dera Temple.
These figures demonstrate that the route generated by
our proposed method effectively passes through loca-
tions with POIs while heading toward the end node.

**4.3.3 Experiments on Execution Time**

We compared the execution times of the proposed
method with those of a baseline method, utilizing the
<figure><img src="image_27.png" /><figcaption>Figure 6: Results of the experiment conducted using the proposed method with Google Maps and Flickr data in Kyoto City, Kyoto Prefecture, Japan. The gray, blue, and orange lines show the road network, shortest route, and recommended route derived using the proposed method. Red dots represent POIs related to 'temple'.</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>Figure 7: Details of waypoints passed by the proposed method in Kyoto.</figcaption></figure>

<table><caption>Table 1: Coordinates of temples located in the target section.</caption><thead><tr><td>temple name</td><td>latitude</td><td>longitude</td></tr></thead><tbody><tr><td>Rokuharamitsu-ji</td><td>34.99722</td><td>135.77336</td></tr><tr><td>Rokudouchinnou-ji</td><td>34.99839</td><td>135.77537</td></tr><tr><td>Shoumyou-ji</td><td>34.99422</td><td>135.77081</td></tr><tr><td>Kiun-ji</td><td>34.99153</td><td>135.78051</td></tr><tr><td>Jippo-ji</td><td>34.99445</td><td>135.77829</td></tr><tr><td>Honju-ji</td><td>34.99464</td><td>135.77910</td></tr><tr><td>Saifuku-ji</td><td>34.99931</td><td>135.77395</td></tr><tr><td>Nittai-ji</td><td>35.02649</td><td>135.79178</td></tr><tr><td>Daizen-ji</td><td>34.99803</td><td>135.77891</td></tr><tr><td>Hofuku-ji</td><td>34.99634</td><td>135.77643</td></tr><tr><td>Hokan-ji</td><td>34.99872</td><td>135.77926</td></tr><tr><td>Kongo-ji</td><td>34.99851</td><td>135.77869</td></tr><tr><td>Tsumyoji</td><td>34.99405</td><td>135.77898</td></tr><tr><td>Kosho-ji</td><td>34.99698</td><td>135.78269</td></tr><tr><td>Juen-ji</td><td>34.99839</td><td>135.77116</td></tr></tbody></table>

road network of Times Square and artificially generated POIs. The baseline method employed two approaches: a simple execution of Dijkstra's algorithm between the start and end points, and an integrated approach combining DBSCAN clustering with Dijkstra's algorithm. For this comparison, we measured only the execution time of the main processing after the user's start and end points had been provided.

In the proposed method, we generated a shortest path tree using Dijkstra's algorithm and subsequently executed the dog-walker pathfinding algorithm, measuring the duration required. For this analysis, we configured the proposed method with parameters that ensured all waypoints were visited, meeting the conditions of the baseline method. In Baseline Method 1, we calculated the execution time of Dijkstra's algorithm from the start point to the endpoint. In the baseline method 2, the route from the start to the end node was calculated multiple times using Dijkstra's algorithm to ensure all given waypoints were visited,
<figure><img src="image_29.png" /><figcaption>Figure 8: POIs-on-map using artificial data.</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Figure 9: Distribution of the shortest distance between each POI and route.</figcaption></figure>

and we compared the execution times. Here, the processing time for DBSCAN in the baseline method was not included in the computation time because it can be performed during preprocessing. Thus, the execution time for the baseline method is proportional to

the time spent on Dijkstra's algorithm. Additionally, The waypoints generated for the experiment has 30 POIs each, randomly generated within a 50m radius from the center point to account for GPS errors.

Figure 8 illustrates the POIs-on-map when the
<figure><img src="image_31.png" /><figcaption>Figure 10: Execution time comparison among proposed and baseline method.</figcaption></figure>

number of waypoints was varied from 3 to 8.

Figure 10 compares the execution times of the proposed method and the baseline methods. The experimental results indicate that, while the execution time of the proposed method is less efficient than the simple Dijkstra's algorithm, it outperforms when applied to routes involving waypoints.

In scenarios similar to those in Google Maps, where the number of POIs associated with each waypoint is low, the proposed method's execution time is notably shorter. Conversely, as the number of POIs belonging to each waypoint increases, the difference in execution time is expected to decrease.

In the baseline method 2, after deciding which size of waypoints to pass through using parameters, the order of movement must be manually configured. On the other hand, the proposed method excels as it integrates the discovery of waypoints and the determination of which waypoints to pass through within a single algorithm, allowing for automatic execution.

### 4.3.4 Experiments on Route Evaluation

We compared the distances between the proposed route and each point of interest (POI) with those generated by the baseline method. Figure 9 (a) presents a histogram of the distances between the route shown in Figure 4 (a) and each POI, while Figure 9 (b) corresponds to Figure 6 (b). These figures reveal that the POIs are more densely clustered around a 50-meter range in the routes generated by the proposed method compared to the baseline approach.

## 5 CONCLUSION & FUTURE WORK

In this study, we proposed a route recommendation method designed to effectively navigate through waypoints, conceptualized around the idea of dogwalker.

The algorithm, named dog-walker pathfinding algorithm, operates based on the principles of two agents: a dog and a walker. The walker, serving as a navigating agent within the graph, moves independently in a two-dimensional space, separate from the dog. However, connected by a leash, the walker is propelled toward the direction the dog wishes to explore. Motivated by POIs acting as incentives, the dog tends to move toward areas abundant in POIs. Once the POIs are sufficiently consumed, the dog moves toward the goal, pulling the walker along a path that is expected to pass through high-density POI areas. This path is the output of our algorithm.

Experiments were conducted in two distinct areas: Kyoto City in Kyoto Prefecture, Japan, and Manhattan, New York, USA. As a result, it has been demonstrated that our method can recommend routes that effectively pass through POIs and operates faster than the baseline method using Dijkstra's algorithm. Future work will focus on further evaluation and refinement of this method. Generally, evaluating algorithms that use social big data is challenging. In this paper, we conducted a qualitative evaluation using Flickr and Google Maps as test datasets. For quantitative evaluation, there are several ideas, such as scoring based on the number of POIs passed or assigning scores based on reviews from Google Maps for specific buildings such as temples. However, these evaluations may vary depending on the context and could be influenced by parameters, potentially making them less reliable.

Various possibilities are available for refining our method, including optimizing parameter settings and the decision-making processes of the agents. Our method focuses on proposing an algorithm that effectively passes through waypoints in cases where POIs and the graph exist on the same plane but do not overlap; however, evaluation and algorithmic improvement are important, and hence will be prioritized in future work. Additionally, there is room for improvement in peripheral issues such as preprocessing the road network used and refining the data extraction methods.

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Characterizing Machine Guidance in Geospatial Analysis

Yue Hao<sup>a</sup> and Guoray Cai

College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, U.S.A.
{yqh5470, gxc26}@psu.edu

**Keywords:** Geographic Information Systems, Geospatial Analysis, Machine Guidance.

**Abstract:** Geospatial analysis poses challenges for individuals with limited expertise in Geographic Information Science (GIScience) methods and tools, requiring complex decision-making and spatial reasoning, often leading to difficulties or even failures. This research explores *machine guidance* as a novel approach to support domain analysts throughout the geo-analytical process. This approach can be implemented as an intelligent interface agent that is capable of recognizing user's analytical difficulties and providing proactive assistance. We propose a conceptual framework that characterizes the cooperative role of machine guidance in geospatial analysis. We focus on answering two key research questions: (1) *when to guide?* and (2) *how to guide?*. The framework provides a foundation for future research on machine-guided geospatial analysis, informing the development of other computer-aided systems that enhance usability and analytical effectiveness in GIScience.

# 1 INTRODUCTION

Geospatial analysis is crucial to scientific investigation and decision-making in various domains (Goodchild and Longley, 1999), ranging from human-environment interactions (De Smith et al., 2007), understanding the health impact of COVID-19 and vaccines (Sun et al., 2020; Wang et al., 2021; Mollalo et al., 2021), healthcare resources (Kang et al., 2020), to public health policies (Ramírez and Lee, 2020; Ahasan and Hossain, 2021). These demands for geospatial analysis stimulated the rapid growth of analytical tools and methods (Goodchild et al., 2000). As Geographic Information Systems (GISystems) grow into a type of high-functionality system, it becomes increasingly harder to learn and use (Lieberman et al., 2015).

Despite its significance, geospatial analysis presents challenges due to its complexity. It involves processing and analyzing geographic data by representing spatial phenomena, utilizing tools and statistical methods, and interpreting spatial relationships (Bailey et al., 1995; Goodchild, 2006). Analysts must recognize patterns in spatial data and understand their underlying significance (Haining, 1994). At the technical level, geospatial analysis makes use of a variety of analytical tools and techniques to understand geographic patterns and events (Goodchild, 1992). However, these tasks are often cognitively demanding and

require specialized knowledge and skills in geography and GISystems, which are not universally accessible.

The complexity of geospatial analysis tools, combined with the need for GIS expertise, creates barriers for analysts, particularly those outside the GIS field. Existing solutions primarily focus on two approaches. The first approach involves empowering analysts by offering education and training programs in geography and GIScience (Council et al., 2005). While these programs are valuable, they are not scalable due to their high costs in terms of time and money. Second, GISystem user interfaces have been enhanced to better reflect how humans perceive, interact with, and conceptualize the world to improve usability and analytical capabilities (Goodchild, 2009). However, even with improved interfaces, analysts still face significant burdens, such as forming effective analytical strategies, preparing data, and executing the required spatial functions. These limitations highlight the need for innovative solutions to make geospatial analysis more accessible and efficient. To make geospatial analysis practical to a diverse range of applications and analysts, solutions are needed to bridge the cognitive and skill gap.

Motivated by addressing the human cognition and expertise barriers during spatial analysis, we propose **machine guidance** (Ceneda et al., 2018) as a novel approach to make geospatial analysis more accessible. The key idea of our approach is to introduce an intelligent interface agent that offers timely help and

a https://orcid.org/0000-0002-8368-4924

Hao, Y. and Cai, G.
Characterizing Machine Guidance in Geospatial Analysis.

Paper published under CC license (CC BY-NC-ND 4.0)

In *Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025)*, pages 39-50

ISBN: 978-989-758-741-2; ISSN: 2184-500X

Proceedings Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
guidance when analysts encounter difficulties in advancing their analytical goals. A machine guidance agent is able to recognize when users are facing difficulties and take the initiative to guide them with appropriate actions (Liu et al., 2018; Ceneda et al., 2019). By leveraging the guidance agent's expertise, human analysts' efforts and cognitive load in identifying clues or suitable solutions when facing challenges can be reduced. Thus, the machine guidance agent is particularly valuable for domain experts who are proficient in their specific problem domain but with limited expertise in GISystems or GIScience (Nyerges, 1995; Traynor, 1998).

Effective machine guidance agent requires two key capabilities. First, the guidance agent must be able to recognize when analysts are facing challenges and whether guidance is needed. Second, it must be able to determine what types of guidance will be provided based on the nature of user challenges. The overall goal is to make an analytic process productive and effective even in cases where users' expertise is inadequate. Towards defining the conceptual framework for machine guidance in geospatial analysis, we explore two key questions to inform its design and implementation:

1. When is guidance needed during the geospatial analytical process? This involves understanding the nature of geospatial analysis process and identifying the points when users might face challenges.

2. How should guidance be provided? Answering this question involves understanding alternative messages and media for communicating guidance and how to choose them to ensure effectiveness and keep them minimally intrusive.

By addressing these two research questions, we aim to characterize a conceptual framework for machine guidance in geospatial analysis. We begin by conceptualizing the geoanalytical process to identify when guidance is most needed. Using a hypothetical scenario, we illustrate how machine guidance can be integrated into the analytical process in various forms. A better understanding of when and how guidance should be designed contributes to the development of a science of design for machine guidance. It can fill the gap that there is a lack of understanding of the machine guidance behaviors in facilitating geospatial analysis. It also can be reused or further developed regarding the mixed-initiative guidance design in the GIS domain. Potentially, it can advance the GIScience agenda on the design and use of effective machine guidance to amplify human capacity for spatial problem-solving.

# 2 LITERATURE REVIEW

## 2.1 Conceptualize Geospatial Analysis

Geospatial analysis is a complex problem-solving process that integrates spatial thinking to address scientific questions and support decision-making (Goodchild and Longley, 1999; Fischer et al., 2011). Unlike other analytical domains, geospatial analysis requires viewing problems with a “geographical eye”, emphasizing spatial relationships, patterns, and interconnections (Downs, 1997). This process involves systematically handling spatial data and extracting meaningful insights, ensuring that spatial objects and their relationships are accurately represented and understood (Haining, 1994). As an iterative analytical method, it involves multiple stages, including problem identification, strategy formation, analysis, and evaluation (De Smith et al., 2007; Bednarz et al., 2013). We categorize this process into Figure 1. Each step demands cognitive and computational efforts to effectively interpret spatial phenomena, manage uncertainties, and optimize solutions (Goodchild and Jabelle, 2010).

The problem-solving aspect of geospatial analysis requires structuring ill-defined spatial problems into well-represented phenomena using spatial concepts such as distance, orientation, connectivity, and scale (Huisman et al., 2009; Miller and Wentz, 2003). Analysts must carefully choose external representations and operations based on the inherent uncertainties in geographic data, as spatial representations and scales influence analytical outcomes (Couclelis, 2003; Grekousis, 2020). Effective geospatial analysis also depends on the strategic integration of data, logical task sequencing, and cognitive reasoning to synthesize and evaluate spatial information dynamically (Dörner and Kreuzig, 1983; Thomas, 2005). Since spatial problems lack predefined solutions, iterative refinement, and expert judgment are necessary to improve decision-making and ensure analytical accuracy (Pretz et al., 2003; Bednarz, 2004).

Figure 1 illustrates the workflow and components involved in geospatial analysis conducted by a human analyst. Analysts, as domain experts, typically start by defining a problem within their specific field. The problem should then be translated into a spatial representation based on the analytical phenomena and their interactions. Next, the spatial problem can be broken down into a series of questions that GIS tools can address. Addressing these questions often requires executions and operations in a coherent workflow within GISystems. Based on the GIS outputs, the analyst compares and selects strategies. The analyst also needs to synthesize and evaluate the outputs
<figure><img src="image_32.png" /><figcaption>Figure 1: The geoanalytical process that requires a series of tasks and decisions.</figcaption></figure>

and intermediate results to actively evaluate and ad-
just their analytical workflow as well as the problem-
solving strategies.

## 2.2 Cognitive Challenges of Geospatial Analysis and the Need for Support

Geospatial analysis includes an essential goal of mak-
ing critical decisions based on locations. It improves
one's understanding of the geographic phenomenon
presented by the data by discovering and revealing the
previously unknown patterns (Huisman et al., 2009).
Cognitively, it is challenging to see things and situ-
ations spatially for structuring problems, finding an-
swers, and expressing solutions (Council et al., 2005).
Generating new knowledge from the result requires
human reasoning and understanding (Ward et al.,
1999). It requires thinking and operational skills from
three different dimensions: spatial concepts, spatial
representation, and spatial reasoning (Council et al.,
2005).

However, constraints of human perception and
cognition, such as limited field of view in percep-
tion and working memory in cognition, have been
documented in psychological studies (Baars, 1997;
Baddeley, 1992; Creem-Regehr et al., 2005). These
limitations impact how we perceive and comprehend
complex data and situations. Choosing the suitable
representation, for instance, is a demanding task that
requires one's cognitive efforts in deciding the ex-
plicit relation and structures of the space as well as
the unknown parts (e.g. data scale and aggregation
level) that one is going to explore (Freksa et al., 2017;
De Smith et al., 2007). Spatial representation should
match the spatial phenomenon as well (Dungan et al.,
2002). At an operational level, proper choice and
use of analytical tools are critical (De Smith et al.,
2007). Generated outputs entail spatial patterns that
vary greatly depending on particular methods that are
applied (De Smith et al., 2007). Making these critical
decisions is inherently a demanding process that re-
quires adequate training to gain spatial thinking skills

(Dramowicz et al., 1993). External assistance is of-
ten required in facilitating the analysts to go through
these procedures.

## 2.3 Inadequate Support to Geospatial Analysis

Efforts to support an effective spatial analytical pro-
cess exist in two tracks. One track is to train ana-
lysts to think spatially about geographic phenomena
and their representations through education and train-
ing (Council et al., 2005). The second track is to im-
prove analytical tools and their interfaces in support-
ing geospatial analytical tasks.

GIScience education and geographical education
are interconnected and complement each other in pro-
viding students with a comprehensive understanding
of geography and the tools needed to address geo-
graphic challenges in various domains (Roche, 2014;
Shin et al., 2016). Based on the geographic foun-
dations, the concept of spatial thinking serves as a
framework for structuring problems, finding solu-
tions, and expressing answers. GIScience education
and geographical education share common goals of
enhancing spatial thinking, using geospatial data, and
preparing students to address real-world challenges.
Efforts and research findings suggest that GISciences
and geospatial education are more than learning tech-
nical skills but the cognitive ability to solve practi-
cal questions at different stages (Downs et al., 1988;
Verma and Estaville, 2018). However, researchers are
afraid that the use of GISystems as a means will be-
come the end so that the users are not able to think
and solve problems spatially without the help of phys-
ical tools (Downs, 1997). The process is also hard to
scale up considering the relevant cost of money and
time (Johnson and Sullivan, 2010).

On the other hand, the design and usability
of GISystems are improved by making them more
closely resemble the way humans reason about the
world (Goodchild, 2009). User handbook and sup-
porting documentation can be one important facilita-
tion. It shows its power and usefulness when users are new to a certain field when timely instructions can significantly improve their analytical process (Ceneda et al., 2016). The emergence of tutorials and guidelines (Perry et al., 2002; Kurland and Gorr, 2007) offer a means of doing spatial analysis by learning. Help resources offered by the software vendors (e.g. ArcGIS Online services) make the GISystems easier to learn and use (Goodchild, 2000). Implementation of question-based GISystem (Scheider et al., 2021; Scheider et al., 2019; Schulze, 2021; Gao and Goodchild, 2013), workflow-based GISystem used for analyzing general or domain-specified problems (Lim et al., 2005; Yeo and Yee, 2016; Badea and Badea, 2013; Kruiger et al., 2021), and sketch-based GISystem (Curtis, 2012) offered new ways for users in interacting with the GISystems in order to ease analyst's difficulties when doing geospatial analysis. For instance, users can express spatial relations more efficiently by applying the electronic pen to the map display (Cohen et al., 1997).

When treating geospatial analysis as an activity, however, it is an incremental process that involves a continuous loop of observing and evaluating the outcomes so that the analytical goal can be refined (Figure 1). Help is needed from the system's side as a mediation tool in reducing the cognitive difficulties (Rogers, 2004). Forming the question itself also involves the efforts in simplifying and dividing the problem into sub-goals and the analytical approach should actively manage and link a series of tasks logically (De Smith et al., 2007). Therefore, the question-based systems and workflow-based systems fail to solve complex spatial analytical problems. Advancement of the design should go beyond the simple automation process without the involvement of interactions and feedback on new information. However, there is still a lack of design in current systems that support or guide interactive spatial analytical behaviors. Human ability and skills like communication and coordination contribute to collaborations with other people and can be extended to the computational systems in forming human-computer collaborations that are the joint efforts of the computer system(s) and human user(s) towards a shared analysis goal (Terveen, 1995). One promising research avenue is to investigate a mixed-initiative approach that combines system-initiated guidance with user-initiated guidance to enhance human-machine intelligence (Pérez-Messina et al., 2022).

## 3 TOWARDS MACHINE GUIDANCE TO GEOSPATIAL ANALYSIS

We define Machine Guidance in geospatial analysis as a mixed-initiative approach designed to help analysts navigate complex spatial tasks when they meet difficulties. The concept of machine guidance originates from early automation and industrial control systems, particularly in manufacturing and robotics (Kendoul, 2012; Li et al., 2009). Over time, fields such as Visual Analytics (VA), enabling user-interactive guidance developed to enhance data visualization and analysis through computer assistance (Ceneda et al., 2016; Collins et al., 2018). Unlike autonomous agents or other systems that provide assistance only upon request, guidance proactively identifies when help is needed and delivers timely, context-aware support throughout the analytical workflow, enabling analysts to make well-informed decisions.

Research on conceptualizing the machine guidance design as well as their physical implementations thrived in VA and data science domains which have plentiful discussions and years of accumulation (Ceneda et al., 2016; Ceneda et al., 2018; Ceneda et al., 2020; Pérez-Messina et al., 2022; Sperrle et al., 2022). However, the GIS domain has yet to fully formalize its integration. There is no formal discussion on how the guidance approach in guiding geospatial analysis can be achieved and thus calls for an attribution. Users of the GIS applications are no longer restricted to the GIS experts but a wider range of users with expertise in specific domains (Slocum et al., 2001). We define the target users of the machine guidance as those who have occasional needs to conduct geospatial analytical tasks based on their domain interests and expertise. This group of users are experts in a particular problem domain but are novices in the tool domain related to GISystems (Nyerges, 1995). They are without or with only a little knowledge of geography or GISceince (Traynor, 1998). More importantly, they have a goal of doing geospatial analysis for critical decision-making.

When supporting geospatial analysis to solve domain problems, guidance should assist human analysts across various analytical stages (Pérez-Messina et al., 2022). Its essential capability is recognizing when the user needs help and what kind of help should be offered at the moment (Ceneda et al., 2016). Followed Figure 1, Figure 2 highlighted the moments when guidance can be introduced. Since geospatial analysis consists of interconnected sub-tasks and continuous decision-making, guidance must dynamically adapt to changes throughout the process. It should
<figure><img src="image_33.png" /><figcaption>Figure 2: When or where machine guidance can enhance a geospatial analytical workflow (outlined in blue).</figcaption></figure>

provide both high-level analytical strategies and com-
putational support, such as selecting appropriate tools
and parameters (Ceneda et al., 2020). Therefore, the
core challenge in designing effective guidance lies in
accurately identifying analytical difficulties and de-
livering support with appropriate content.

In the following sections, we first identify when
guidance can be needed based on the conceptualized
geospatial analytical process. We then frame and ex-
plain how guidance can be provided in the process. To
demonstrate when and how a human analyst interacts
with and benefits from machine guidance, we present
a hypothetical scenario:

Lucy, a public health promotion analyst, is working to improve the local food environment in Wisconsin. Her objective is to identify counties with limited food access, focusing on those with a high prevalence of food deserts. Additionally, she aims to prioritize densely populated counties, as interventions in these areas would have a greater impact. With limited state funding, she must strategically allocate resources to maximize effectiveness.

Based on this scenario, Table 1 outlines several mo-
ments when guidance can be introduced (MG) along-
side the analyst’s requests and feedback (User).

**3.1 When to Provide Guidance**

Addressing the question of *when guidance is needed*,
we propose that timely intervention is essential

whenever analysts face challenges that hinder their
progress. As noted in Figure 2, guidance can be in-
troduced throughout the process from different as-
pects. Particularly, we introduce a conceptual frame-
work that informs the design and development of ma-
chine guidance that operates on three levels:

*   **Problem-solving Level** Guidance helps analysts spatialize and refine problems. User 1 indicates the analytical goal of understanding the food shortage conditions so MG 1 provided guidance by retrieving a dataset representing counties and food deserts, which offered insights into the current food environment in each county. This enabled the analyst to develop a foundational understanding of the phenomenon and prepared the analyst for subsequent exploratory steps using the available data. Additionally, analytical goals may need refinement based on intermediate findings. For instance, the initial problem may be ill-defined or unrealistic. Guidance can assist analysts in evaluating their goals and making necessary adjustments.

*   **Operational Level** Guidance supports analysts in developing GIS workflows and executing specific spatial functions within a GISsystem. When analysts are uncertain about how to tackle a particular question, guidance can help construct workflows by suggesting appropriate methods and operations. Additionally, if analysts are unfamiliar with specific tasks, guidance can automate processes to abstract operational complexities and
<table><caption>Table 1: Interactions between the Lucy (User) and the machine guidance agent (MG) when assisting her in locating targeted counties.</caption><thead><tr><th>Dialogue</th><th>Guidance Response</th></tr></thead><tbody><tr><td><p><strong>User 1:</strong> I want to find counties in Wisconsin with food shortages.</p><p><strong>MG 1:</strong> Counties in Wisconsin and the food deserts in the state are presented. We found 110 food deserts.</p><p><strong>User 2:</strong> For each county, I need to know how many food deserts are there.</p><p><strong>MG 2:</strong> We counted how many food deserts by county as a measurement.</p><p><strong>User 3:</strong> I need to find counties with a large number of food deserts and are densely populated. How can I do that?</p><p><strong>MG 3:</strong> We have two strategies. (a) Manual Selection: You can analyze the distribution of food desert counts in relation to population density and manually select counties that meet your criteria. While <em>a-1</em> offers more flexibility in analyzing each variable individually, <em>a-2</em> enhances pattern recognition by integrating both factors into a single visual representation, making it easier to identify areas of concern at a glance. If the goal is to detect broad trends and prioritize interventions efficiently, <em>a-2</em> is the preferable choice. However, if a more detailed breakdown of each factor is required before drawing conclusions, <em>a-1</em> may be more useful. (b) Automated Selection: We have identified 6 counties where both the number of food deserts and the population density exceed the mean which may pinpoint areas of interest.</p><p><strong>User 4:</strong> Strategy (b) results are easier to follow but I can not deal with all these counties. How about using more than 5 food deserts for selection?</p><p><strong>MG 4:</strong> After adjusting the selection condition, we found 5 counties.</p><p><strong>User 5:</strong> I will do more exploration visually. Thank you.</p><p><strong>MG 5:</strong> Any other requests? ...</p></td><td><p><strong>Food Deserts in Wisconsin by County</strong></p><p><strong>Number of Food Deserts in Each County</strong></p><p><strong>Legend</strong></p><p><strong>MG 1</strong><br><em>Number of Food Deserts in Each County and Distribution of Population Density</em></p><p><strong>MG 2</strong><br><em>Number of Food Deserts in Each County in Relation to Population Density</em></p><p><strong>MG 3 (a)-1</strong><br><em>Counties with over Average Population Density and over 3 Food Deserts</em></p><p><strong>MG 3 (a)-2</strong><br><em>Counties with over Average Population Density and over 5 Food Deserts</em></p><p><strong>MG 3 (b)</strong></p><p><strong>MG 4</strong></p></td></tr></tbody></table>
streamline execution. For instance, User 2 explicitly expressed the goal of determining the number of food deserts in each county, prompting the guidance system to assist by performing the necessary operations, such as a Spatial Join, to directly achieve this objective (MG 2). This support alleviated the analyst's burden of figuring out how to obtain the counts and which operations to execute within the GISystem.

* **Strategy Level** Guidance aids analysts in devising and selecting problem-solving strategies. In MG 3, the guidance offers two different strategies with three different methods. Multiple solutions may exist for a given problem, requiring analysts to simulate different processes and explore “what-if” scenarios (MG 4) to determine the most suitable approach. The guidance agent facilitates this process by assisting analysts in exploring, evaluating, and comparing different strategies to identify the optimal solution.

From another perspective, guidance can be initiated either through explicit requests or by inferring implicit hints. In MG 1, the system proactively assists the analyst by interpreting the phenomenon as a starting point, fetching data it deems relevant to the current analytical goal without requiring explicit input. Instead, it infers the specific data needs based on the stated objective. In contrast, MG 2 through MG 4 were triggered by the analyst's direct feedback, where the guidance responded to explicit requests for assistance regarding their current intention. It ensures that guidance can take the initiative to help by adapting to inferred needs as well as meet user-initiated demands, enhancing human-system collaboration.

## 3.2 How to Provide Guidance

### 3.2.1 Guidance in Different Formats

Based on how the guidance is delivered and communicated to its user, we can categorize the guidance design based on its output format (Pérez-Messina et al., 2022). Guidance can be offered with:

* **Numeric reports and textual information**, like describing what to do next in words or a list of values. In our scenario, the guidance provides continuous textual explanations, enabling the analyst to clearly understand both the ongoing processes and the specific support being offered by the system.

* **Visual displays**, like showing a map, chart, or diagram on the screen. With the visual clues, the user does not need to rely on their memories or imaginations when scoping their questions.

Throughout MG1 to MG4, the guidance system generates maps as outputs, enabling the analyst to visualize spatial patterns and facilitating easier comparisons. This visual evidence improves the analyst's ability to track and advance the analytical process, ensuring it aligns effectively with the intended goals.

* **Physical operations**, like direct operation on the system end to derive the expected output or answer. In MG2, for instance, the Spatial Join operation was done as part of the guidance behavior.

* **Other formats**. Other possible formats like videos, animations, and audio also can be considered.

The listed formats are not exclusive from each other and can be combined to provide more details if needed. For instance, texts and maps are combined to guide the analyst in our scenario. In MG3, textual explanation offers hints on how to compare and decide which strategy can be more suitable. Need to mention that incorporating excessive content into a single guidance—such as using multiple formats, offering overly detailed information, or combining too many elements—can lead to cognitive overload. To ensure clarity and usability, guidance should avoid overwhelming analysts with excessive details that may hinder their ability to make informed judgments. Instead, it should prioritize simplicity and focus on delivering concise, relevant information tailored to the user's immediate needs.

### 3.2.2 Guidance in Different Degrees

Degree of guidance means how much assistance is provided (Ceneda et al., 2019; Ceneda et al., 2016). The guidance that is **orienting** or **directing** the user has a higher degree of freedom and flexibility compared to **prescribing** guidance that demonstrates a fixed solution.

* **Orienting guidance orients “where to go”**. It only suggests high-level hints and suggestions. It is aimed at maintaining users' current thinking process (“mental map”) and offering the potential solutions that can be adopted (Ceneda et al., 2016). At the problem-solving level, it guides the user to think about and decide on what is the proper next step.

* **Directing guidance directs “what can be chosen”**. It offers concrete recommendations like the possible options and alternatives that can lead to desired results (Ceneda et al., 2016). It is more detailed and concrete than the orienting guidance. In MG3, the guidance provides two strategies in
three methods to identify densely populated coun-
ties with a high number of food deserts. By pre-
senting multiple options, it not only offers flexi-
bility but also empowers the analyst to retain con-
trol, allowing them to choose and decide the most
suitable approach for their next steps.

* **Prescribing guidance prescribes “what to do”.**
  It can offer step-by-step directions to solve a
  specific problem. Unlike cases where the ana-
  lyst is unsure of the next steps, here the analyst
  knows the goal but may not know how to achieve
  it. This prescriptive process can largely be au-
  tomated (Ceneda et al., 2016). For instance, the
  process of calculating counts (MG2), and the task
  of applying a different selection condition (MG4),
  are both automated. This approach conceals the
  operational complexities and presents only the fi-
  nal outputs.

3.3 Design Challenges and
Opportunities

Designing a computational system capable of provid-
ing effective machine guidance for spatial problem-
solving presents several challenges. One of the pri-
mary difficulties lies in recognizing when and how to
intervene without disrupting the analyst’s workflow.
The assistance should only be provided when it is
genuinely needed and when the analyst is ready to re-
ceive it (Maes, 1995). Otherwise, unnecessary inter-
ruptions could confuse the analyst and interfere with
the analytical process (Ceneda et al., 2020). Detect-
ing the need for assistance can be achieved through
explicit user requests or implicit behavioral moni-
toring, such as tracking user interactions, detecting
recurring difficulties, or even analyzing physiolog-
ical indicators like stress-related facial expressions
(Ceneda et al., 2016; Ceneda et al., 2021). A well-
designed system must balance responsiveness with
non-intrusiveness to ensure a seamless analytical ex-
perience (Maes, 1995).

Another significant computational challenge in-
volves equipping the system with reasoning and plan-
ning capabilities. The system must be able to track
the progress of an analysis, recognize what has been
completed, identify gaps in the current strategy, and
suggest appropriate next steps. This requires integrat-
ing structural knowledge, which enables the system
to monitor and manage analytical workflows (Arm-
strong et al., 1990). Furthermore, a control process
is needed to oversee and guide execution, ensuring
that if an approach fails, alternative solutions can be
explored (Hayes-Roth and Hayes-Roth, 1979). The
system must also infer the analyst’s current stage and

anticipate their information needs, adjusting guidance
accordingly. Recent advancements in Artificial In-
telligence (AI), particularly Large Language Mod-
els (LLMs) and GeoAI, offer promising solutions for
enhancing the design of machine guidance agents.
These technologies can improve the agent’s ability
to interpret user queries, infer analytical goals, and
adaptively learn from user interactions to refine fu-
ture assistance (Li and Ning, 2023). A learning mod-
ule could be integrated to analyze user behavior over
time, helping the system provide more personalized
and context-aware recommendations (Smith et al.,
1987). Leveraging AI-driven techniques can signif-
icantly enhance the effectiveness of computational
guidance systems by making them more intuitive,
adaptive, and capable of supporting complex spatial
analyses.

How to store and represent the required expertise
is another essential challenge. The mentioned ex-
pertise should encompass various types of informa-
tion, including declarative knowledge (basic informa-
tion about spatial and non-spatial objects), procedu-
ral knowledge (strategies for applying spatial opera-
tions), and control knowledge (heuristics for evaluat-
ing the validity of actions and solutions) (Hofer et al.,
2017). The system must be able to intelligently select
and compare spatial operations based on their pur-
pose and suitability for different analytical goals. For
instance, when selecting datasets for Wisconsin, the
representation could vary based on the scale and pur-
pose of analysis, whether as a point on a small-scale
map or as a polygon in a larger-scale map. Knowl-
edge of metadata such as geometry type, spatial ex-
tent, and scale is crucial for ensuring that the correct
datasets are retrieved and applied computationally.

4 DISCUSSION

To ensure the successful implementation of the ma-
chine guidance approach, several critical research
questions must be addressed. First, the conceptual
framework requires further evaluation and refinement.
While our characterizations (Sections 3.1, 3.2) pro-
vide an initial understanding of the machine guidance
approach in geospatial analysis, it remains uncertain
whether they accurately reflect real-world analytical
workflows, the specific challenges analysts face, and
the most effective formats for delivering guidance.
One way to bridge this gap is through observational
studies to examine how analysts approach geospatial
problem-solving in real-world scenarios. Addition-
ally, empirical research is needed to determine the
optimal timing and presentation of guidance to maxi-
mize its effectiveness. A user-centered approach will
offer valuable insights for designing more intuitive
and effective machine-guided systems.

Secondly, it is computationally challenging to determine when to provide the guidance. Although we have outlined potential strategies in Section 3.3, their implementation is still limited, particularly in the context of geospatial analysis. Future research should explore methods to intelligently recognize the need for guidance, ensuring that it is provided in a timely and non-disruptive manner. This could involve developing adaptive systems that learn from user behavior and context to offer guidance only when it is most beneficial.

Third, ensuring the scalability of the expertise embedded within the guidance while maintaining its specificity across diverse domains is critical. This raises important questions about what information or knowledge should be captured, how it should be represented, and the level of detail required to balance generality and domain-specific relevance. Addressing these questions is essential for creating guidance systems that are both adaptable to a wide range of applications and sufficiently detailed to provide meaningful support in specialized contexts. Future research should explore methods for capturing and structuring domain knowledge in a way that ensures scalability without sacrificing precision and ultimately ensures machine guidance applications are effective across various analytical domains.

To sum up, our study is to establish proof-of-concept design principles for machine guidance. While we have identified various design opportunities, our work remains at the conceptual stage and lacks computational implementation and practical evaluation. Future efforts are needed to develop concrete computational frameworks for machine guidance and to conduct empirical evaluations. Implementing these systems in real-world applications will provide valuable insights through user feedback, which can help refine and validate the proposed design features. Addressing the mentioned challenges will not only advance the field of machine-guided geoanalytical systems but also pave the way for broader applications in other domains where human-machine collaboration is critical.

5 CONCLUSIONS

We propose using *machine guidance* as a new approach to support analysts’ geospatial analytical process. As a complex and mentally demanding endeavor, geospatial analysis poses a significant challenge

for individuals with limited GIS knowledge and
experience. This challenge highlights the importance
of machine guidance, which encapsulates reasoning
and processing steps within the system itself, allow-
ing analysts to focus on interpreting analytical outputs
rather than navigating complex workflows.

As a starting point, we characterized a conceptual framework focusing on (1) *when* guidance should be introduced and (2) *how* guidance can be delivered to help the analyst. Specifically, our approach suggests that machine guidance should be placed when the user meets difficulties when doing geospatial analysis. At these moments, users may struggle with decision-making due to the need for extensive mental effort, often lacking sufficient knowledge or expertise to solve the issue at hand. Using the hypothetical scenario, we presented various machine guidance that are in different degrees and formats.

To the best of our knowledge, this research marks the first attempt to integrate machine guidance into GISystems, with the potential to drive advancements in the broader field of GIScience. As an initial effort, this study introduces the concept of the machine guidance approach in supporting geospatial analysis as well as its design opportunities and potential. Our ongoing efforts are focused on refining these guidance design strategies and evaluating their feasibility in real-world scenarios. We seek to foster further academic discussion and social engagement around the role of machine guidance in facilitating geospatial analysis. Ultimately, we hope this work sparks further investigation and encourages the development of innovative approaches along the way.

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Satellite Images and Spectral Vegetation Indices as Auxiliary Tools
to Monitoring Fuel Availability in Areas Prone to Wildfire:
Study Case in the Northern Region of Portugal

Bárbara Pavani-Biju<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> Ⓓª, José G. Borges<fnref n="4" /> Ⓓᵇ Susete Marques<fnref n="4" /> Ⓓᶜ and Ana C. Teodoro<fnref n="1" />,<fnref n="2" /> Ⓓᵈ

Keywords: Remote Sensing, Wildfire, Spectral Vegetation Indices, Sentinel-2, Wildfire Fuels.

**Abstract:** Remote sensing data has led to the development of spectral indices for monitoring ecosystems, land surface changes, and water quality. These indices are used in various applications, including agricultural and wildfire monitoring, to understand vegetation cycles and disturbances. Wildfire research focuses on the effects of extreme occurrences, and understanding forest ecology after severe events is crucial for evaluating forest health. Vegetation Indices (VIs) are frequently used in forest and wildfire monitoring studies to account for plant biophysical, biochemical, and physiological characteristics. Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), Normalized Difference Infrared Index (NDII), and Plant Senescence Reflectance Index (PSRI) are indices used to assess vegetation conditions. VIs are valuable resources for monitoring post-wildfire occurrences, as they measure biophysical changes and provide comprehensive monitoring of the affected area, playing a crucial role in assessing the health of forests. Pre-wildfire vegetation conditions monitoring is also important for implementing preventative measures in critical regions to increase wildfire defense and identifying wildland fuels is crucial for improving fuel management actions. This research aims to demonstrate the effectiveness of chosen VIs and fuel models as tools to assess pre-fire conditions, enabling decision-makers to increase wildfire surveillance and landscape resilience in Vale do Sousa, Portugal's northern area. Despite limitations, this approach is valuable, especially in terms of financial or logistical constraints. Moreover, combining VIs with fuel hazard models can improve fuel reduction efforts.

# 1 INTRODUCTION

With the rapid development and accessibility of remote sensing data, several spectral indices have been developed to monitor ecosystems, land surface changes, and water quality (Chughtai et al., 2021; Ma et al., 2019). Spectral indices have been used in a variety of applications, including agricultural and wildfire monitoring, to better understand vegetation cycles or disturbances (Zeng et al., 2022).

A significant percentage of wildfire research focuses on the effects of extreme occurrences (Dos Santos et al., 2020). Understanding the forest ecology after severe events is crucial for evaluating forest health (Avetisyan et al., 2023). The information acquired may help stakeholders make key decisions about post-fire restoration, management, and actions. Most post wildfire assessments rely on field surveys, which are expensive, time-consuming, and limited to specific regions of the affected area (Fernandes et al., 2006).

<footnote n="1">¹Department of Geosciences, Environment and Land Planning, University of Porto,
Rua Campo Alegre, 687, 4169-007, Porto, Portugal</footnote>

<footnote n="2">²Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, 4169-007 Porto, Portugal</footnote>

<footnote n="3">³Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal</footnote>

<footnote n="4">⁴Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal</footnote>

<footnote>barbarapavani@isa.ulisboa.pt, joseborges@isa.ulisboa.pt, smarques@isa.ulisboa.pt, amteodor@fc.up.pt</footnote>

<footnote>ªhttps://orcid.org/0000-0001-7721-008X</footnote>

<footnote>ᵇhttps://orcid.org/0000-0002-0608-5784</footnote>

<footnote>ᶜhttps://orcid.org/0000-0001-7922-5680</footnote>

<footnote>ᵈhttps://orcid.org/0000-0002-8043-6431</footnote>
Vegetation indices (VIs) are frequently used in forest and wildfire monitoring studies to account for plant biophysical (detecting vegetation structure and changes), biochemical (e.g., pigments, water), and physiological (photosynthetic light use efficiency) characteristics (Zeng et al., 2022). The Normalized Difference Vegetation Index (NDVI) is one method for assessing vegetation conditions (Rouse et al., 1973). The Normalized Burn Ratio (NBR) and Differenced Normalized Burn Ratio (dNBR) are used to evaluate damage to vegetation and regeneration (Roy et al., 2006). The Plant Senescence Reflectance Index (PSRI) is used to measure chlorophyll concentration (Merzlyak et al., 1999).

VIs are a valuable resource for monitoring post-wildfire occurrences since they can measure biophysical changes, allowing for comprehensive monitoring of the affected area (Avetisyan et al., 2023). Most common VIs products are easily accessible through frameworks like Copernicus Browser, which produces NDVI, Moisture Index, and other metrics from Sentinel-2 data (ESA, 2024).

For example, NDVI can also be calculated from Landsat imagery using Google Earth Engine (Gorelick et al., 2017) or obtained as a product directly from the United States Geological Survey (U.S. Geological Survey, 2024). Other VIs can be computed using Geographic Information System (GIS) tools like QGIS, ArcGIS®, and SNAP (ESA, 2024; Esri & Environmental Systems Research Institute, 2015; QGIS Association, 2024).

VIs are calculated as a ratio of certain bands, differences, or derivatives of reflectance from sensor imagery at specific spectral wavelengths (de Almeida et al., 2020). Sentinel-2 constellation provides high-resolution images for monitoring vegetation at visible and infrared wavelengths, such as NDVI, which is calculated by combining the Near Infrared (NIR) and red bands.

With a rising rate of severe weather events associated with climate change, VIs also play an important role as reliable tools for assessing the health of forests (Lee et al., 2024). As an example, the forest in Portugal's northern region is particularly vulnerable to severe wildfires, which are becoming increasingly regular in this region (Marques et al., 2017; San-Miguel-Ayanz et al., 2017; Teodoro & Duarte, 2013).

As mentioned before, post-wildfire event monitoring is crucial for forest management and decision-making processes. However, studying vegetation before a fire occurs is just as important as assessing post-fire conditions, physical features, and climate data for the affected area (Lee et al., 2024).

When stakeholders monitor pre-wildfire vegetation conditions, they can implement preventative measures in critical regions to increase wildfire defense. VIs are helpful to evaluate the condition of vegetation in terms of greenness (NDVI), humidity (Normalized Difference Difference Formulation Index - NDII), and senescence (PSRI) (Bento-Gonçalves et al., 2019b; Hardisky et al., 1983; Merzlyak et al., 1999).

Wildfire behavior and impacts have a connection to the type of vegetation and its attributes; in the words of Fernandes et al., (2006), describing wildland fuels is crucial in order to improve fuel management actions. This means that it is essential to classify the study area's land use and land cover (LULC) in order to identify vegetation cover type and structure, as well as classify them according to their fire hazard potential. When combined NDVI, PSRI and NDII to identify fuel availability, with other information, such as fuel hazard potential, they may assist in identifying areas where fuel management should be prioritized (Bento-Gonçalves et al., 2017).

The main objective of the present research is to demonstrate the effectiveness of the chosen VIs as a tool to assess pre-fire vegetation conditions. To assess fuel availability, a vegetation fuel hazard model developed for Portugal by Fernandes et al., (2006) was considered. Enabling decision-makers to take action to increase wildfire surveillance and landscape resilience in Vale do Sousa, Portugal's northern area, principally in critical areas such as the Wildland Urban Interface.

# 2 MATERIALS AND METHODS

## 2.1 Study Area

The research area (Figure 1), a forested area with about 29k ha, Vale do Sousa, which is situated in northern Portugal, encompasses two Forest Intervention Zones (ZIFs): Entre-Douro-e-Sousa and Castelo de Paiva. Extreme fires have taking place in the Vale do Sousa woodland area over the past few years (Pavani-Biju et al., 2024). Large wildfires occurred in 2017, burning more than 5000 hectares and causing major environmental and financial impacts (ICNF, 2017).

Vale do Sousa has also a large rural area, with Eucalyptus (Eucalyptus globulus Labill) as the dominating tree species, followed by Maritime Pine (Pinus pinaster A.), small areas of other deciduous oak such as cork oak (Quercus suber L.), and pedunculate oak (Quercus robur L.), and riparian areas.
<figure><img src="image_34.png" /><figcaption>Figure 1: Study area Vale do Sousa.</figcaption></figure>

However, the study area includes small portions of the NATURA 2000 Network, a network of protected areas that covers Europe's most valuable and vulnerable landscapes and spans 27 European Union Member States and in order to protect this area, crucial actions need to be taken to mitigate large wildfires harmful effects (CEE, 1992).

## 2.2 Data Acquisition and Analysis

For this study, images from Sentinel-2 between 2017 and 2018 were obtained at Copernicus Browser (table 1), based on the closest date the wildfire started, with less than 5% cloud cover. First, images were pre-processed from Level-1C (Top of Atmosphere - TOA) to Level-2A (Bottom of Atmosphere - BOA) resulting in orthorectified surface reflectance images, at SNAP using the algorithm SEN2COR (ESA, 2024). After, VIs were produced for September and October of 2017 in order to evaluate pre-fire vegetation conditions.

<table><caption>Table 1: Sentinel-2 images collected for 2017 and 2018.</caption><thead><tr><td>Sentinel Images</td><td>Date Wildfire started</td></tr></thead><tbody><tr><td>September 02<sup>nd</sup>, 2017</td><td>September 02<sup>nd</sup>, 2017</td></tr><tr><td>October 12<sup>th</sup>, 2017</td><td>October 15<sup>th</sup>, of 2017</td></tr></tbody></table>

Furthermore, leaf-on multispectral images from July 2017 and July 2018 were classified using Machine Learning (ML), Support Vector Machine in ArcGIS Pro® to determine changes between 2017 and 2018. To train the classifier 200 samples were collected for each class were collected from the thematic map of Land Use and Occupation (COS) of Portugal, from 2015 and 2018. The same spatial information were adapted to establish the accuracy assessment points.

The COS is the most widely used national reference cartography for land use issues. It is a vector product updated every three years that represents 83 thematic classes with a minimum cartographic unit of 1 hectare based on the visual interpretation of orthophoto maps. There are five temporally consistent editions available (1995, 2007, 2010, 2015, and 2018), with an accuracy higher than 85% (SNIG, 2022). The sampling approach used was random points. Approximately 500 points were generated from the image. Further, the confusion matrix and accuracy assessment were calculated. Accuracy ranges from 0 to 1, with 1 representing 100% accuracy, and the Kappa statistic indicate the overall accuracy of the classification. (McCoy & Johnston, 2001). The landscape classes chosen to