GIS and Public Health

This Special Issue on GIS and public health is the result of a highly selective process, which saw the participation of some 20 expert peer-reviewers and led to the acceptance of one half of the high-quality submissions received over the past year. Many threads link these papers to each other and, indeed, to our original call for papers, but the element that most clearly emerges from these works is the inextricable connection between public health and the environment. Indeed, GIS analysis of public health simply cannot disregard the geospatial dimension of environmental resources and risks. What consistently emerges from these analyses is that current geospatial research can only scratch the surface of the complex interactions of spatial resources, risks, and public health. In today’s world, or at least in the developed world, researchers and practitioners can count on virtually endless data, on inexpensive computational power, and on seamless connectivity. In this research environment, these papers point to the need for improved analytical tools, covering concepts, representation, modeling and reliability. These works are important contributions that help us to identify what advances in geospatial analysis can better address the complex interactions of public health with our physical and cultural environment, and bridge research and practice, so that geospatial analyses can inform public health policy making. [...

Schools, air pollution, and active transportation: An exploratory spatial analysis of calgary, Canada

An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children

L'analisi spaziale

The so-called data deluge, along with ever-increasing technological capabilities, tantalizes geographers with exciting opportunities for spatial data analysis. These opportunities come with challenges, because data and technology, alone, cannot address the pressing questions of our world. Spatial analysis, a.k.a. spatial statistics, is a lot more than colourful maps and attractive displays: still relatively underrepresented in the Italian geography, this discipline has grown from a strictly quantitative niche to part of a critical spatial science and continues to stimulate new developments in statistics because, as we know, spatial is special. This book, published in the series “New Geographies. Work Tools”, adds spatial analysis to the Italian geographer’s toolbox. Not a how-to manual, it presents some of the core analytical issues through the redundancy of narrative language and mathematical language. It traces the journey of spatial analysis from its roots in quantitative geography, GIS, and statistics, towards the definition of its own identity and the acceptance of its own relativity and limitations. It discusses the relationship of spatial analysis with GIScience and its efforts to embed critiques within its own discourse, emphasizing the role of theory, the importance of hypothesis testing, and acknowledging the ethics surrounding the use and analysis of data. A few examples illustrate practical implementations, showing the value added by spatial statistics in yielding reliable analyses that can support management decisions. It concludes with a brief outlook on the Italian geographic literature, where spatial analysis – like elsewhere – can play a role in competently accepting today’s opportunities and challenges, in a constructive dialogue within geography as a whole

Comparison of distance measures in spatial analytical modeling for health service planning

<p>Abstract</p> <p>Background</p> <p>Several methodological approaches have been used to estimate distance in health service research. In this study, focusing on cardiac catheterization services, Euclidean, Manhattan, and the less widely known Minkowski distance metrics are used to estimate distances from patient residence to hospital. Distance metrics typically produce less accurate estimates than actual measurements, but each metric provides a single model of travel over a given network. Therefore, distance metrics, unlike actual measurements, can be directly used in spatial analytical modeling. Euclidean distance is most often used, but unlikely the most appropriate metric. Minkowski distance is a more promising method. Distances estimated with each metric are contrasted with road distance and travel time measurements, and an optimized Minkowski distance is implemented in spatial analytical modeling.</p> <p>Methods</p> <p>Road distance and travel time are calculated from the postal code of residence of each patient undergoing cardiac catheterization to the pertinent hospital. The Minkowski metric is optimized, to approximate travel time and road distance, respectively. Distance estimates and distance measurements are then compared using descriptive statistics and visual mapping methods. The optimized Minkowski metric is implemented, via the spatial weight matrix, in a spatial regression model identifying socio-economic factors significantly associated with cardiac catheterization.</p> <p>Results</p> <p>The Minkowski coefficient that best approximates road distance is 1.54; 1.31 best approximates travel time. The latter is also a good predictor of road distance, thus providing the best single model of travel from patient's residence to hospital. The Euclidean metric and the optimal Minkowski metric are alternatively implemented in the regression model, and the results compared. The Minkowski method produces more reliable results than the traditional Euclidean metric.</p> <p>Conclusion</p> <p>Road distance and travel time measurements are the most accurate estimates, but cannot be directly implemented in spatial analytical modeling. Euclidean distance tends to underestimate road distance and travel time; Manhattan distance tends to overestimate both. The optimized Minkowski distance partially overcomes their shortcomings; it provides a single model of travel over the network. The method is flexible, suitable for analytical modeling, and more accurate than the traditional metrics; its use ultimately increases the reliability of spatial analytical models.</p