An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services

Abstract

Background: There is interest in the use geospatial data for development of acute stroke services given the importance of timely access to acute reperfusion therapy. This paper aims to introduce clinicians and citizen scientists to the possibilities offered by open source softwares (R and Python) for analyzing geospatial data. It is hoped that this introduction will stimulate interest in the field as well as generate ideas for improving stroke services. Method: Instructions on installation of libraries for R and Python, source codes and links to census data are provided in a notebook format to enhance experience with running the software. The code illustrates different aspects of using geospatial analysis: (1) creation of choropleth (thematic) map which depicts estimate of stroke cases per post codes; (2) use of map to help define service regions for rehabilitation after stroke. Results: Choropleth map showing estimate of stroke per post codes and service boundary map for rehabilitation after stroke. Conclusions The examples in this article illustrate the use of a range of components that underpin geospatial analysis. By providing an accessible introduction to these areas, clinicians and researchers can create code to answer clinically relevant questions on topics such as service delivery and service demand.

Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities

“…Open-data sources with global scope offer opportunities to measure and analyse urban health and sustainability indicators in diverse geographical contexts. [14][15][16][17][18] In particular, OpenStreetMap (OSM) is a crowdsourced mapping project that provides open access to regularly updated spatial data worldwide, coded according to consistent and community-led guidelines. 19 This article addresses the need to better measure, map, and compare urban design and transport features important for creating healthy and sustainable cities.…”

The right tools for the job: The case for spatial science tool‐building

“…Merchán, Winkenbach, and Snoeck (2020) and Merchán and Winkenbach (2019) investigate last-mile logistics circuity in São Paulo and develop better approximation algorithms for urban route distance prediction. Padgham et al (2019) examine hospital siting by simulating network-constrained stroke service center catchment basins, while Lin, Zhang, Zhu, and Meng (2019) identify the determinants of bicycle catchment basins around Shanghai's metro stations. Liao, Gil, Pereira, Yeh, and Verendel (2020) model street networks in several world cities to compare travel time by personal automobile and public transit.…”

“…Padgham et al. (2019) examine hospital siting by simulating network‐constrained stroke service center catchment basins, while Lin, Zhang, Zhu, and Meng (2019) identify the determinants of bicycle catchment basins around Shanghai’s metro stations. Liao, Gil, Pereira, Yeh, and Verendel (2020) model street networks in several world cities to compare travel time by personal automobile and public transit.…”

Exploring Urban Form Through Openstreetmap Data: A Visual Introduction

“…The mobile crowd-sensing platform CrowdSenSim uses OSMnx to simulate urban environments (Montori et al, 2019;Tomasoni et al, 2018) and the transportation planning company Remix developed its street design platform-now deployed in hundreds of cities worldwide-using OSMnx to model city streets. Padgham et al (2019) use OSM data to demonstrate siting hospitals for faster time-sensitive access, such as for stroke treatment. OSM's street and urban form data were widely used in the humanitarian response to 2010's catastrophic Haitian earthquake (Zook et al, 2010).…”