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Mapping accessibility to Clalit’s clinics
Closer to Care
This tool shows the average time from each pixel (sized 200m2) to get to a Clalit Clinic (symbolized using Clalit’s logo) by walking, driving and public transport rides in the city of Be’er-She’va. Light yellow shows areas from which residents can reach a Clalit clinic in 10min while dark red shows areas from which it takes 30min to reach a Clalit clinic, for all travel modes. For each travel mode and time threshold, a summary statistic of the % of people by age group that can walk/drive/take public transit to a clinic is shown. Finally, hovering over each clinic shows the commercial and public amenities in the cluster around the clinic, as a proxy for the attraction factor of each location.
Accessibility to routine medical treatment and preventive care through HMO clinics, is essential but often challenging for people with limited mobility. This includes the elderly, people with limited mobility due to health reasons, communities with limited public transit, individuals without private transportation, and residents of rural or low-density urban regions.
In the challenging desert climate of Be’er-Sheva, ease of access by walking and public transport is important as people are willing to spend less time commuting in extreme temperatures.
In the challenging desert climate of Be’er-Sheva, ease of access by walking and public transport is important as people are willing to spend less time commuting in extreme temperatures.
What this tool estimates
Why is accessibility
to clinics important?
to clinics important?
This tool shows the average time from each pixel (sized 200m2) to get to a Clalit Clinic (symbolized using Clalit’s logo) by walking, driving and public transport rides. Light yellow shows areas from which residents can reach a Clalit clinic in 10min while dark red shows areas from which it takes 30min to reach a Clalit clinic, for all travel modes.
For each travel mode and time threshold, a summary statistic of the % of people by age group that can walk/drive/take public transit to a clinic is shown. Finally, hovering over each clinic shows the commercial and public amenities in the cluster around the clinic, as a proxy for the attraction factor of each location.
For each travel mode and time threshold, a summary statistic of the % of people by age group that can walk/drive/take public transit to a clinic is shown. Finally, hovering over each clinic shows the commercial and public amenities in the cluster around the clinic, as a proxy for the attraction factor of each location.
What this tool estimates
This project measures accessibility to Clalit Health Clinics in the city of Be’er-She’va, by travel time to the closest clinic by walking, driving or public transport. It identifies areas with limited access, city-wide accessibility by age groups and shows the commercial and public amenities around each clinic. Mapping access to clinics using this interactive tool can assist decision-makers in Clalit HMO to make an informed decision about where to place a new clinic to improve access for certain demographic groups and drive decision makers in Be’er-Sheva to invest in bettering the public transport system.
Background
Road network and Traffic counts:
We calculated accessibility using a combination of road network data from OpenStreetMap (OSM) and traffic counts from official GTFS. The OSM road network includes all roads classified by type (e.g., highways, secondary roads, residential streets, etc.) and further annotated with attributes such as directionality, average speed limits, and mode-specific accessibility (e.g., driving, cycling, or walking). Official GTFS includes the full schedule of buses by hour and day of the week, as well as the bus routes and stops. Traffic counts for driving were included within OpenTripPlanner.
POIs and Clinic data:
We assembled a dataset of Points of Interest (POIs) and Clinic data from a combination of Google Places data, OSM and the City of Be’er-Sheva. POIs from Google and OSM have longitude and latitude coordinates along with their classification (park, grocery store, health clinic, restaurant, etc.) while POIs from Be’er-Sheva Municipality also have detailed names and additional info. Clalit clinic locations were selected from our POI dataset and availability of nearby amenities around each clinic were calculated using a straight-line (Euclidean) distance of 200meters.
Population Data:
Population counts with each pixel was provided by the City of Be’er-Sheva, counting the number of residents from each age group that resides within the pixel. For ease of visual design, we aggregated and presented only four age groups but the original data detailed residents in eight fine-grain resolution: 0-4;5-9;10-18;19-30;31-44;45-64;65-74;75+
We calculated accessibility using a combination of road network data from OpenStreetMap (OSM) and traffic counts from official GTFS. The OSM road network includes all roads classified by type (e.g., highways, secondary roads, residential streets, etc.) and further annotated with attributes such as directionality, average speed limits, and mode-specific accessibility (e.g., driving, cycling, or walking). Official GTFS includes the full schedule of buses by hour and day of the week, as well as the bus routes and stops. Traffic counts for driving were included within OpenTripPlanner.
POIs and Clinic data:
We assembled a dataset of Points of Interest (POIs) and Clinic data from a combination of Google Places data, OSM and the City of Be’er-Sheva. POIs from Google and OSM have longitude and latitude coordinates along with their classification (park, grocery store, health clinic, restaurant, etc.) while POIs from Be’er-Sheva Municipality also have detailed names and additional info. Clalit clinic locations were selected from our POI dataset and availability of nearby amenities around each clinic were calculated using a straight-line (Euclidean) distance of 200meters.
Population Data:
Population counts with each pixel was provided by the City of Be’er-Sheva, counting the number of residents from each age group that resides within the pixel. For ease of visual design, we aggregated and presented only four age groups but the original data detailed residents in eight fine-grain resolution: 0-4;5-9;10-18;19-30;31-44;45-64;65-74;75+
Data Sources
Accessibility to the nearest clinic was calculated using OpenTripPlanner (OTP) from the centroid of each populated cell within Be’er-Sheva's fishnet grid. The analysis was conducted for three modes of transportation: walking, driving, and public transit. To account for service variability, the public transit travel time was determined by taking the median value from ten routing options calculated for Monday morning between 8:00 AM and 10:00 AM.
Subsequently, accessibility from each cell was illustrated using predefined travel time cut-offs, which were set at 10 and 15 minutes for walking, and 10, 15, 20, and 30 minutes for both driving and transit. For the purposes of demographic analysis, population data was aggregated into four logical age cohorts: 0-4, 5-18, 19-64, and 65+.
Credits & collaborations
Research Team:
Talia Kaufmann — Direction, head of ACP lab
Michael Drogochinsky — Data Analytics, research assistant
Artem Nikitin — Design and Web Cartography, research assistant
The Algorithmic City Planning (ACP) lab is part of the Center for Urban Innovation at HUJI.
This project is developed in collaboration with NUR Lab - Negev Urban Research headed by Merav Battat and Yonatan Cohen, of the Ben-Gurion University in the Negev.
Subsequently, accessibility from each cell was illustrated using predefined travel time cut-offs, which were set at 10 and 15 minutes for walking, and 10, 15, 20, and 30 minutes for both driving and transit. For the purposes of demographic analysis, population data was aggregated into four logical age cohorts: 0-4, 5-18, 19-64, and 65+.
Credits & collaborations
Research Team:
Talia Kaufmann — Direction, head of ACP lab
Michael Drogochinsky — Data Analytics, research assistant
Artem Nikitin — Design and Web Cartography, research assistant
The Algorithmic City Planning (ACP) lab is part of the Center for Urban Innovation at HUJI.
This project is developed in collaboration with NUR Lab - Negev Urban Research headed by Merav Battat and Yonatan Cohen, of the Ben-Gurion University in the Negev.
Methodology