Computational and data sciences for health-GIS

Abstract

Computational and data sciences are transforming the entire science enterprise. In the arena of GIS, this is represented by the emergence of cyberGIS. We provide an overview of applying the cyberGIS approach to spatial analysis for health studies. We emphasize that cyberGIS is not just a service to traditional spatial analyses, but itself is an alternative approach to problem solving. Some fundamental and profound distinctions of cyberGIS approaches in health-GIS include the following: (1) they may greatly reduce the reliance on models or assumptions, and instead seek actual empirical evidence through mining a large amount of data or virtual empirical evidence generated through computation; (2) they tend to be non-parametric and tend to generate local solution; (3) they are scalable to high-resolution and less aggregated data; (4) they tend to be stochastic rather than deterministic; and (5) with these approaches, the large amount of data may not be only from input data-sets, but also from analytical workflows. We described the kernel ratio estimation for local intensity estimation, the restricted and controlled Monte Carlo for data disaggregation, and unrestricted and controlled Monte Carlo for statistical significance evaluation as examples of the cyberGIS approaches in health-GIS.

What drives disease flows between locations?

“…As flows that originated at source locations actively generate new cases at target locations, these chained spatial flows of disease dictate where and when certain communities and individuals will be affected. While the timely prediction of disease flows is essential for the effective intervention of epidemics and pandemics, few studies have paid attention to the dynamics of flows and what drives the flows (Li et al, 2019; Shi & Kwan, 2015; Shi & Wang, 2015; Zhu, Huang, Shi, Wu, & Liu, 2018).…”

Mapping the spatial disparities in urban health care services using taxi trajectories data

“…Besides adequate methods, the availability of individual data is also important for modeling the demand–supply of health care services (Shi & Wang, ). In this study, we propose a novel approach for characterizing the demand–supply of health care services using taxi trajectories data.…”

New methods for projecting enrollments within urban school districts

“…The first question, in Chapter 2, is answered by representing change in gpr as a spatially continuous variable captured using spatially adaptive kernel ratio estimation (KRE) methods (Silverman 1986, Bithell 1990, Tiwari and Rushton 2005, Davies and Hazelton 2010, Diggle 2013, Shi and Wang 2015 followed by recovery of the enrollments. This seeming tautology demonstrates that the spatial pattern of gpr change is sufficiently accurate and detailed for capturing detailed enrollment changes within the district.…”

“…Based on the requirement to estimate gpr with a required sample size of 20 students for a study area whose grade-specific enrollment density varies over space and time, Kernel Ratio Estimation (KRE, Shi and Wang (2015)) is the estimator that should be used. A well-known method of spatial analysis, KRE enables the continuous modelling of discrete data.…”

“…Consider an approach whereby this chapter is omitted and Chapter 3 begins in medias res. This chapter tests the utility of measuring and estimating gprs and changes in gprs from a defined ex post interval with adaptive kernels (Silverman 1986, Bithell 1990, Tiwari and Rushton 2005, Davies and Hazelton 2010, Shi and Wang 2015. There would be no validation of internal consistency, i.e.…”