This document describes a study that developed a method using Geographic Information Systems (GIS) to characterize obesogenic urban environments by integrating measures of the food and physical activity environments. The study designed a multivariate tool to assess these environments in median neighborhoods of around 15,000 residents in an urban area. The tool used kernel density estimation to calculate a synthetic index from factors representing availability and access to food and opportunities for physical activity. The index was intended to help inform future urban health interventions and research by identifying neighborhoods that may promote or hinder healthy behaviors.

1. Healthy urban environment characterization
focused on physical activity and food: A GIS-
based method
Alba Cebrecos1,2 (alba.cebrecos@uah.es), Julia Díez2, Pedro Gullón2,
Usama Bilal2,3, Manuel Franco2,3 and Francisco Escobar1,2
1. Department of Geology, Geography and Environment, Faculty of Biology,
Chemistry and Environmental Sciences, University of Alcalá, Alcalá de
Henares, Madrid, Spain.
2. Social and Cardiovascular Epidemiology Research Group, Universidad de
Alcala, Madrid, Spain
3. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

2. http://hhhproject.eu
Background
OBESITY
ENERGY INTAKE
ENERGY
EXPEDITURE
OBESOGENIC
ENVIRONMENT
FOOD
ENVIRONMENT
PHYSICAL ACTIVITY
ENVIRONMENT
INDIVIDUAL
BEHAVIOURS
BUILT
ENVIRONMENT
Individual SES
Socioeconomic
environment

3. http://hhhproject.eu
Background
Conectivity
Food
stores
Diversity
Accesibility
Ratio
Facilities
Sprawl
Urban
design
Safety
Price
FOOD
ENVIRONMENT
PHYSICAL ACTIVITY
ENVIRONMENT
INTEGRATEDAPPROACH
GIS-based
OBESOGENIC
ENVIRONMENT

4. http://hhhproject.eu
Objective
To design a multivariable tool based on
Geographic Information Systems (GIS) to integrate
information from the physical activity and food
environment to better characterize obesogenic
environments in urban areas.

5. http://hhhproject.eu
Median Neighbourhood
Index (MNI)
12 contiguous census
sections
≈15,000 residents
Study area

6. http://hhhproject.eu

7. http://hhhproject.eu
Methods

8. http://hhhproject.eu
Methods
Kernel Density Estimation
(KDE)

9. http://hhhproject.eu
Results

10. http://hhhproject.eu
Discussion and conclusions
 Development of an innovative method to assess the obesogenic
environment by a synthetic index integrating measures of food and
physical activity environments
 Results highlight a way to consider urban environments as
integrated promoters or barriers for healthy behaviors such as
better eating and being physically active
 Synthetic index may be relevant tool to inform future urban health
interventions or research.
 Future research may consider more complex weighting of factors
when considering the importance of the food and physical activity
environment

11. http://hhhproject.eu
THANKS!
Questions?

12. http://hhhproject.eu
Results
7915024 7915030 7915033 7915034 7915035 7915036 7915037 7915038 7915039 7915112 7915113 7915114 TOTAL
Population (%) 980 (6.5) 1110 (7.4) 1265 (8.4) 635 (4.2) 1205 (8.0) 540 (3.6) 1335 (8.9) 2145 (14.3) 1980 (13.2) 1680 (11.2) 1480 (9.9) 625 (4.2) 14975
Women (%) 555 (56.6) 600 (54.0) 780 (61.6) 395 (62.2) 670 (55.6) 320 (59.2) 750 (56.2) 1210 (56.4) 1135 (57.3) 980 (58.3) 865 (58.4) 315 (50.4) 8580 (57.3)
Foreign born (%) 200 (20.4) 205 (18.5) 165 (13.0) 115 (18.1) 230 (19.1) 485 (18.5) 535 (20.5) 495 (23.1) 190 (9.6) 200 (11.9) 205 (13.8) 165 (26.4) 2615 (17.5)
Years <16 (%) 180 (18.4) 60 (5.4) 135 (10.7) 25 (6.8) 130 (10.8) 70 (13.0) 125 (9.4) 230 (10.7) 105 (5.3) 140 (8.3) 40 (2.7) 70 (11.2) 1320 (8.8)
Years > 65 (%) 180 (18.4) 310 (28.0) 435 (34.4) 255 (40.1) 300 (24.9) 160 (29.6) 250 (18.7) 350 (16.3) 505 (25.5) 230 (13.7) 395 (10.8) 275 (26.7) 3645 (24.3)
Area in km2 (%) 0.05 (12.0) 0.04 (9.3) 0.05 (12.2) 0.03 (7.4) 0.03 (6.3) 0.02 (4.7) 0.03 (6.9) 0.04 (9.6) 0.03 (7.9) 0.03 (8.1) 0.03 (8.2) 0.03 (7.5) 0.42
Density Hab/m2 19366.1 28224.2 24478.0 20359.1 45451.1 27387.5 45980.6 53011.4 59102.7 49018.2 42545.8 19691.2 35416.4
Characterization LOW MEDIUM-LOW MEDIUM-LOW HIGH
MEDIUM-
HIGH
MEDIUM-LOW LOW LOW
MEDIUM-
HIGH
LOW
MEDIUM-
HIGH
MEDIUM-LOW