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2020 | Book

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Geospatial Technologies for Urban Health

Editors: Dr. Yongmei Lu, Dr. Eric Delmelle

Publisher: Springer International Publishing

Book Series : Global Perspectives on Health Geography

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This volume presents a timely collection of research papers on the progress, opportunities, and challenges related to the advancement of geospatial technologies for applications in urban health research and management. The chapter authors cover technologies ranging from traditional GIS and remote sensing technologies, to recently developed tracking/locational technologies and volunteered geographic information (VGI). In four main sections, the book uniquely contributes to the conversation of how geospatial technologies and other GIScience research may be enhanced by addressing the data and challenges presented by urban health issues. The book is intended for those with backgrounds in health and medical geography, social epidemiology, urban planning, health management, and lifestyle research.

The book starts with an introduction by the editors, providing an overview of traditional and emerging geospatial technologies and how they each can significantly contribute to urban health studies. Section 1 covers urban health risk and disease, and analyses the spatial and temporal patterns of selected urban health issues. Section 2 addresses urban health service access, and demonstrates how traditional and new geospatial technologies apply to different segments of urban populations facing varied challenges. Section 3 focuses on incorporating geospatial technologies in promoting healthy behaviours and lifestyles in urban settings. Section 4 assesses how geospatial technologies may be incorporated into urban health policies and management practices. Adopting a forward-looking perspective, these papers examine the various health challenges in urban systems, and explore how new and emerging geospatial technologies will need to develop to address these problems.

Table of Contents

Frontmatter
Introduction
Abstract
This chapter provides an overview of the background and content of this book. Starting with a discussion on the recent edited volumes on or closely related to urban health, this chapter highlights the need for a book on geospatial technologies for the study of urban health. The uniqueness of geospatial approaches to investigate urban health issues can be attributed to the spatial perspective and the lens of place. This chapter further argues that the continuous development in geospatial technologies, coupled with recent advances in communication and information technologies, portable sensor technologies, and the various social media and open data, has played an essential role for the modelling of environment exposure and health risk. However, there still exist challenges for urban health studies. These challenges maybe rooted in, among the multiple causes, a lack of understanding of the micro-level health decisions and the methodological limitation to address the Uncertain Geospatial Contextual Problem. This chapter finishes with a section-by-section and chapter-by-chapter overview of the empirical studies included in this book volume. This overview is provided to illustrate the organization of this book and to serve as a guide for a reader to navigate through the book chapters.
Yongmei Lu, Eric Delmelle

Urban Health Risk and Disease

Frontmatter
Geospatial Approaches to Measuring Personal Heat Exposure and Related Health Effects in Urban Settings
Abstract
Recent and projected changes in temperature extremes, including the intensification of heat waves, present a persistent health threat for urban residents. Due to limitations in data availability and the spatial representativeness of fixed-site temperature observations, there exists a notable gap in the geospatial sciences on the multi-scale characterization of geographic patterns of extreme heat and the associated correlation with individual vulnerability in urban settings. Studies employing individual-level exposure assessment methodologies are sparse. Yet rapid advancements in low-cost wearable sensors and other mobile technologies can be leveraged to capture geo-referenced environmental exposure (e.g., temperature) and health data (e.g., physiologic strain) to better understand and quantify the impacts of variations in individual microclimates. The emergence of new technologies and rich spatial datasets requires multi-disciplinary collaboration to advance the science on place-based exposure to thermal extremes and the associated health impacts for at-risk populations in urban environments.
Margaret M. Sugg, Christopher M. Fuhrmann, Jennifer D. Runkle
Geographic Variation in Cardiovascular Disease Mortality: A Study of Linking Risk Factors and Built Environment at a Local Health Unit in Canada
Abstract
Cardiovascular disease (CVD) is one of the leading causes of death in Canada. CVD risk factors and outcome data are used to determine trends of disease risk to inform public health program planning for prevention and control of disease and risk reduction or elimination. Recent efforts to map CVD and its associated risk factors at the health region level have provided further insights into variation in determinants across populations. In this chapter, geographic information system (GIS) and spatial analysis were utilized to enhance CVD surveillance to identify the patterns and relationships between CVD mortality and its potential risk factors. Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) approaches were used to explore geographical variation in the rate of CVD mortality. After consideration of potential environmental, epidemiological, demographic, and socioeconomic factors, spatial statistics analysis revealed geospatial clustering for CVD mortality and the “hot spots” or “cold spots.” Within a mixed rural-suburban setting in Ontario, Canada, there was an evidence of significant built environmental factors and immigrant time associated with the rate of CVD mortality. Moreover, this pilot work suggests that the integration of geospatial information with routinely collected surveillance data appears feasible within the structure and resources of local public health units as a means to assist in the identification of regional variation in the burden of CVD.
Lei Wang, Chris I. Ardern, Dongmei Chen
Evaluating the Effect of Domain Size of the Community Multiscale Air Quality (CMAQ) Model on Regional PM2.5 Simulations
Abstract
A growing number of urban health impact studies use Community Multiscale Air Quality (CMAQ) models for air pollution exposure estimation, although the performance of CMAQ models is likely to be affected by multiple parameters, including the configuration setting of the study domain. We presented an approach for CMAQ model uncertainty assessment with respect to domain size and reported spatial and temporal variations of CMAQ model performance over two study domains, a relatively small domain (DS) and a large domain (DL). Specifically, we simulated daily PM2.5 concentrations over two domains during 2011 and quantified the difference between the model predictions. The model performance was assessed by comparing modeled PM2.5 against measured PM2.5 values at monitoring sites located in the region of overlap for each domain. The results suggest that the CMAQ simulations over two domains were in good agreement across the study area except in southwestern areas. We also found that the overall model performance was better for CMAQ simulations with a large domain relative to the smaller domain. Based on our findings, we recommend applying a large domain for PM2.5 simulations, particularly for urban health risk assessments conducted over summer months, which generally contain more emissions.
Xiangyu Jiang, Eun-Hye Yoo

Urban Health Service Access

Frontmatter
Serving a Segregated Metropolitan Area: Disparities in Spatial Access to Primary Care Physicians in Baton Rouge, Louisiana
Abstract
This study examines spatial accessibility of primary care in the Baton Rouge Metropolitan Statistical Area, Louisiana. Two popular accessibility measures are used: the proximity method focuses on the travel time from the nearest facility and the two-step floating catchment area (2SFCA) method considers the match ratio between providers and population as well as the complex spatial interaction between them. The two methods capture different elements of spatial accessibility: one being physically close to a facility and another adding availability of service. Both properties can be valuable for residents. In the study area, residents in urban areas generally enjoy shorter travel time from their nearest service providers as well as higher accessibility scores measured by the 2SFCA method (i.e., physicians per 1000 residents) than rural residents. Overall, disproportionally higher percentages of African Americans are in areas with shorter travel time to the nearest primary care providers and higher accessibility scores; so are residents in areas of higher poverty rates. This “reversed racial advantage” in spatial accessibility does not capture nonspatial obstacles related to financial and other socioeconomic factors for African Americans (and population in poverty) and nevertheless represents one fewer battle to fight in reducing healthcare disparities for various disadvantaged population groups. Such an advantage disappears or is even reversed in remote rural areas with high concentration of African Americans, who suffer from double disadvantages in both spatial and nonspatial access to primary care.
Fahui Wang, Michael Vingiello, Imam M. Xierali
Considerations When Using Individual GPS Data in Food Environment Research: A Scoping Review of ‘Selective (Daily) Mobility Bias’ in GPS Exposure Studies and Its Relevance to the Retail Food Environment
Abstract
Advancements in geospatial technologies including geographic information systems and global positioning system (GPS) devices have provided insights on how the retail food environment might be contributing to the ongoing obesity epidemic. Caution has been raised, however, around the potential for research that uses GPS-captured activity spaces to overestimate the impact that exposure to food retailers has on food choices and behaviour. This phenomenon, where it is difficult to discern whether an individual is passively exposed to a space or actively seeks it out, is referred to as a ‘selective (daily) mobility bias’. Researchers’ understanding of this bias is relatively new and understudied, particularly in the food environment literature, where the bias could have serious implications. This chapter reviews 14 peer-reviewed papers and two doctoral theses to identify and critique the methods proposed for handling this bias and offer recommendations to consider as the use of GPS-activity space studies continues to grow.
Reilley Plue, Lauren Jewett, Michael J. Widener
Dynamic Emergency Medical Service Dispatch: Role of Spatiotemporal Machine Learning
Abstract
Previous research has suggested that providing prompt access to emergency medical services (EMS) may greatly improve the health outcomes of patients with urgent conditions. However, there has not been enough research on ways in which planning resources for ambulance dispatch may enhance the response time of EMS. GIS has been used to manage and visualize the spatial distribution of EMS demand, but there is still a need for more empirical evidence from spatiotemporal demand-based prediction techniques, such as machine learning. We applied the long short-term memory (LSTM) method to forecast EMS demands based on past records and reallocated service locations using a dynamic maximal covering location model. The training of the prediction models and validation were conducted with 323,993 emergency calls in the Gyeongnam Province in Korea in 2014. We found that conventional hotspot-based emergency dispatch systems, ignoring temporal variations of service demands, could fail to fulfill a desired coverage standard. This study shows an evidence that demand-based spatiotemporal demand prediction and dynamic dispatch protocol based on machine learning algorithm have the potential to support more efficient allocation of resources, especially when resources are limited.
Sunghwan Cho, Dohyeong Kim

Healthy Behavior and Urban Lifestyle

Frontmatter
Incorporating Online Survey and Social Media Data into a GIS Analysis for Measuring Walkability
Abstract
Existing walkability measurements have not considered some important components of the built environment, pedestrians’ preferences, or all walking purposes. As area-based measurements, they may overlook some detailed walkability changes. We propose a Perceived importance and Objective measure of Walkability in the built Environment Rating (POWER) method, which is a line-based approach considering both the perception of pedestrians and subjective characterizing of the urban built environment. Incorporating online survey and social media data, we present a built environment walkability study in a specific environment and the potential for more general scenarios. The survey can be customized for the particular urban environment and capture the preferences of a local population. The social media obtain general opinions from a broader audience. Although focusing on the specific setting at a university campus, we also included the general social media results to supplement the POWER structure and survey findings. Using social media and survey results can bring two scales together to provide a more complete understanding of walkability.
Xuan Zhang, Lan Mu
Leveraging Social Media to Track Urban Park Quality for Improved Citizen Health
Abstract
In this chapter, we showcase the use of qualitative data available on two “geobrowsers” (i.e., Google Maps and Foursquare) and of a data-mining technique to quantify the sentiment of online reviews about parks. The underlying interest for this study comes from the growing literature suggesting that living near parks or other open spaces contributes to higher levels of physical activity and to lower levels of stress and fewer mental health problems. Mecklenburg County (North Carolina), which encompasses the City of Charlotte, is used as a case study. In a comparison among 97 cities in the USA, The Trust for Public Land ranks Charlotte’s park system at the very bottom and reports their spending per resident on their park system among the lowest 20% of these cities. Considering their lower spending, the city government may be particularly interested to leverage publicly available data from social media to complement the assessments they already perform about their park system, such as satisfaction surveys or quality assessments. Nevertheless, Charlotte’s low ranking – although unfortunate – indicates an opportunity for the city to improve its park system, which in turn could engage residents in more physical activity and, in doing so, create positive community health outcomes.
Coline C. Dony, Emily Fekete

Health Policies and Urban Health Management

Frontmatter
Spatiotemporal Analysis and Data Mining of the 2014–2016 Ebola Virus Disease Outbreak in West Africa
Abstract
This study investigates the spatiotemporal pattern of the 2014 Ebola virus disease (EVD) epidemic in the most heavily affected countries in West Africa and also mines the spatial associations between such pattern and other geographically distributed factors. Utilizing the publicly available open-source data, this study demonstrates a research design that integrates various geospatial data processing, analysis, and data-mining techniques to achieve the research objectives. For the 2014 EVD epidemic, spatiotemporal patterns were analyzed and visualized. Fine-grained population data were obtained through a population interpolation method to conduct healthcare accessibility analysis. Finally, associations between the spatiotemporal patterns of the incidences and healthcare accessibility as well as other factors were examined. The results suggest that (1) poor accessibility to healthcare facilities and EVD clusters are identified in many urban areas as well as some remote areas; and (2) EVD cases were more likely to be found in border areas of these countries where accessibility to healthcare facilities is poorer.
Qinjin Fan, Xiaobai A. Yao, Anrong Dang
Extending Volunteered Geographic Information (VGI) with Geospatial Software as a Service: Participatory Asset Mapping Infrastructures for Urban Health
Abstract
Community asset mapping is an essential step in public health practice for identifying community strengths, needs, and urban health intervention strategies. Community-based Volunteered Geographic Information (VGI) could facilitate customized asset mapping to link free and accessible technologies with community needs in a mutually shared, knowledge-producing process. To address this issue, we demonstrate a participatory asset mapping infrastructure developed with a Chicago community using VGI concepts, participatory design principles, and geospatial Software as a Service (SaaS) using a suite of free and/or open tools. Participatory mapping infrastructures using decentralized system architecture can link data and mapping services, transforming siloed datasets to integrated systems managed and shared across multiple organizations. The final asset mapping infrastructure includes a flexible and cloud-based data management system, an interactive web map, and community asset data stream. By allowing for a dynamic, reproducible, adaptive, and participatory asset mapping system, health systems infrastructures can further support community health improvement frameworks by facilitating shared data and decision support implementations across health partners. Such “community-engaged VGI” is essential in integrating previously siloed data systems and facilitating means of collaboration with health systems in urban health research and practice.
Marynia Kolak, Michael Steptoe, Holly Manprisio, Lisa Azu-Popow, Megan Hinchy, Geraldine Malana, Ross Maciejewski
Improving Urban and Peri-urban Health Outcomes Through Early Detection and Aid Planning
Abstract
Chronic food insecurity significantly constrains short- and long-term health, as well as the development of individuals and households, ultimately impacting economic progress in some of the poorest and fastest growing communities on the planet. One of the strategies used to combat household- and individual-level food insecurity is food aid. Ensuring that food aid reaches the neediest people, however, is an ongoing challenge. In this chapter, we explore the use of geospatial technologies as part of a framework for improving food aid targeting in Bamako, Mali. We develop and apply quantitative models that rely on remotely sensed data and health survey data to highlight the importance of different aspects of demand for food aid in urban spaces. The results highlight the usefulness of this approach for food aid planning in urban areas where food need is unevenly distributed over a densely populated area.
Kathryn Grace, Alan T. Murray, Ran Wei
Backmatter
Metadata
Title
Geospatial Technologies for Urban Health
Editors
Dr. Yongmei Lu
Dr. Eric Delmelle
Copyright Year
2020
Electronic ISBN
978-3-030-19573-1
Print ISBN
978-3-030-19572-4
DOI
https://doi.org/10.1007/978-3-030-19573-1

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