H.E.L.P: A GIS-based Health Exploratory AnaLysis Tool for Practitioners

Abstract

The last two decades have been characterized by a growing number of Geographical Information System (GIS) applications to the field of health science. From a decision-making and policy perspective, undeniable benefits of GIS include the assessment of health needs and delivery of services, and also the appropriate allocation of workforce and prevention resources. Despite these attractive attributes, the literature suggests that there has been limited GIS uptake among health care decision makers. This paper presents a GIS-based Health Exploratory and anaLysis tool for Practitioners (H.E.L.P.) for the analysis and visualization of space-time point events, applied to hospital patients. H.E.L.P. is viewed as a spatial decision support system which provides a set of powerful analytical tools integrating the computational capabilities of Matlab with the visualization and database functionalities of GIS. The system outputs improve the understanding of disease dynamics and provide resources for decision-makers in allocating appropriate staffing. As an example, H.E.L.P. is applied to a dataset of hospital patients in Cali, Colombia.

Geographical Assessment of Low-Carbon Transportation Modes: A Case Study from a Commuter University

“…Therefore, they are worthy of discussion. For example, unlike past studies (e.g., [15,71]), our analysis did not give an indication that being female, or more bicycle and pedestrian accidents are associated with lower level of LCT mode shares. A higher population density seems to make slightly positive differences in the walk mode, but shows negative association with the bicycle mode.…”

“…For instance, Zhou [14] found that specific services such as affordable housing and proximity to a bus line are incentives for students' alternative mode choices. Since availability and affordability of parking spaces encourages car use, strategies for reducing parking spaces are discussed, including raising parking prices to a point when affordability can be an issue for students [15,48]. This program is criticized for being ineffective to higher income students and the possible long-term impact on students' enrollment, especially in commuter campuses [40].…”

“…Studies, however, reports that today's university students are less likely to own a car and more likely have positive attitudes towards using alternative LCT modes [16,[52][53][54]. Gender makes a large difference in cycle commuters, as female university students more likely to avoid this mode [7,15]. Additional transportation research conducted by social psychologists shown that car use habits, perceived mobility needs, and pro-environmental behaviors affect university students' mode choice [45,49].…”

Regional transport and its association with tuberculosis in the Shandong province of China, 2009–2011

“…The Ripley's K function, proposed for the identification of spatial point patterns, has been used in public health studies (Dixon, 2006;Wheeler, 2007;Delmelle et al, 2011). We examined the patterns of TB cases using the Ripley's K-function statistics at a county level of 16 km and below.…”

Incorporating the human-Aedes mosquito interactions into measuring the spatial risk of urban dengue fever

“…Kernel density estimation is widely used and accepted for describing the spatial patterns of infected human subjects and vectors in epidemiological and vector-borne disease studies (Delmelle, Zhu, Tang, & Casas, 2014;Lai et al, 2004;Minh et al, 2009;Van Steenwinkel, Ribbens, Ducheyne, Goossens, & Dewulf, 2011). In dengue studies, kernel smoothed maps are also used to identify hotspot areas of infected human cases and vectors for assessing the risk of exposure to dengue (Delmelle, Delmelle, Casas, & Barto, 2011;Delmelle et al, 2014;Kan et al, 2008;Wu et al, 2013). Previous studies of the moving distance of humans and vectors (Harrington et al, 2005;Maciel-De-Freitas, Codeco, & Lourenco-de-Oliveira, 2007;Wen et al, 2012) were used to establish dengue risk maps, with a searching radius of 1500 m and vector risk maps with a searching radius of 300 m. All procedures of kernel density estimation were processed using CrimeStat Spatial Statistics Program 3.3.…”