Sustainable Development Report 2023 – Annex
Methodological Summary and Data Tables

A.1 Pedestrian accessibility
indicators for the SDGs (SDG 9.1.1)

transportation infrastructure and food choices) ina under-15-minute walk.

In the 2030 Agenda for Sustainable Development
and the associated New Urban Agenda, the UN
urged cities to provide more accessible, wellconnected infrastructure for bringing people into
public spaces and improving walkability.

Methodology

Pedestrian accessibility is the extent to which thebuilt environment supports walking access todestinations of interest, or the ability of urban
residents to access services and opportunities.
While this metric is highly related to thepromotion of good health and well-being (SDG 3)
and the access to key services in education (SDG4) and transportation infrastructure (SDG 9), its
main use lies in assessing the sustainability andquality of the urban built environment. It isparticularly useful for assessing spatial justice incities, usually represented by underprivileged
communities which are pushed to live indeteriorated urban areas receiving a minor shareof public investments and thus low levels ofaccessibility.
Monitoring spatial indicators of pedestrian
accessibility helps planners and policymakers
evaluate the impacts of urban design andtransport interventions and guides targetedinterventions towards achieving SDG 11 bycreating healthy, sustainable cities. Urban
pedestrian accessibility is represented in the UN
SDG Indicator Framework by two different
indicators: Urban access to public transportation(SDG 11.2.1) and Urban access to public spaces(SDG 11.7.1).
As part of its effort to develop new geospatial
indicators for the SDGs, the SDG Transformation
Center and the 2023 Sustainable Development
Report present this dataset for pedestrian
accessibility indicators. In the 2023 SDR, thisdata is implemented as the proportion of urban
residents with access to a range of key services(education, health, open public spaces, public

Data sources
Two main sources of data are behind this study.
OpenStreetMap is used to collect data on
pedestrian infrastructure and geographicallyallocated places of interest (POI): hospitals,schools, supermarkets, restaurants, schools,etc. Pedestrian infrastructure networks arereturned by the OpenStreetMap API as networks
of nodes and edges, where each node
represents a street intersection and each edge
represents a segment of road with walkablefeatures. Data on population density for every city
is retrieved from the European Commission's
2020 Global Human Settlement Layer (GHSL).
This data is retrieved in the form of a grid of 100m
by 100m cells and their associated population
values covering the entire world.
Geographical extent
The geographical extent of a particular city or
region often varies according to different
authorities and interpretations. Novel projects,
such as the Global Human Settlements (GHS)
Urban Centers Database (UCDB), seek toestablish a consistent, shared geographic
definition of “urban centers” globally. This study
does not consider municipal boundaries fordefining city borders. Rather, it considers"Functional Urban Areas" as defined by theOECD and the European Commission. The
boundaries of Functional Urban Areas consider
urbanization factors such as commuting flows
and population density, and are less arbitrary
than municipal boundaries. For this reason, cities
presented here may have a different (and oftenbigger) shape expected.
Accessibility analysis
To measure accessibility to services for each city,
we perform a network analysis on the pedestrian
street networks and POIs data to quantify andmap accessibility at the street intersection level.

The distance from each street intersection to theclosest point of interest in each of the predefined
categories is measured. These distances areaveraged to generate the final average walking
time to services in general. Distances aretransformed into minutes by applying a
multiplication factor of 0.0125, meaning thatpedestrians are estimated to walk at 3 mph. The
resulting walking times for each category areaveraged for the population grid cells. For each
100m cell from the population grid data, theresulting walking time reflects the time that theaverage person residing inside that cell would
have to walk for, using the existing pedestrian
infrastructure, to reach the first amenity from agiven category of services. The analysis wasperformed using geopandas and pandana
python packages.
These scores were then be generalized for eachcountry, by taking the population weighted
average of the accessibility score for each pointin the population grid.

Data validation
These calculations were performed for all cities
where at least one POI could be identified foreach square kilometer. This threshold is appliedin order to enforce representativity andaccuracy. Countries where less than 40% of theurban population is represented after applying
the aforementioned thresholds were excluded
from the final dataset.

Known limitations
The fact that the underlying POI and streetnetwork data (from OpenStreetMap) arevoluntarily collected represents the biggest
limitation of these indicators. As a result, theaccessibility results that we present may be moreor less accurate depending on the city. Ingeneral,
research
hasshownthatOpenStreetMap data is more representative of
real-world land use patterns, with its street
network being 83% accurate as of 2019. There
are, however, large disparities from place toplace.
Urban, rural and suburban settlements
It’s important to notice that we're not justcomparing countries, but rather the urban
settlements of countries, weighted bypopulation.
Somedeveloping
countries,
especially small ones, might not have very large
urban populations. This means that even if themajority of people might be living in urban
settlements, those won’t be reflected by thesescores, as they’re exclusively about urban
settlements as defined by GHS UCDB.
Some countries will have very few urban centers.
N'Djaména and Bangkok, for instanceconcentrate the vast majority of their respective
countries' urban populations, so when wecompare Chad and Thailand, we're really mostlycomparing these two cities.

References
Nicoletti, L., Verma, T., Sirenko, M. (2022).
Disadvantaged Communities Have Lower
Access to Urban Infrastructure. Environment
and Planning B: Urban Analytics and CityScience, 0(0).
Liu, S., Higgs, C., Arundel, J., Boeing, G.,
Cerdera, N., Moctezuma, D., Cerin, E., Adlakha,D., Lowe, M. Giles-Corti, B. (2021). A
Generalized Framework for Measuring
Pedestrian Accessibility around the World Using
Open Data. Geographical Analysis.