Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments
Bibliographic Data
| ID | 13113992 |
|---|---|
| Authors | Daniel Yonto (0009-0008-3247-0820, University of North Carolina at Charlotte, corresponding author), L Michele Issel (0000-0002-4328-1805, University of North Carolina at Charlotte), Jean-Claude Thill (0000-0002-6651-8123, University of North Carolina at Charlotte) |
| Year | 2019 |
| Volume | 3 |
| Issue | 3 |
| Pages | 75-75 |
| Publication date | 2019-07-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Urban Science (JOURNAL) |
| Journal identifiers | ISSN: 2413-8851 • E-ISSN: 2413-8851 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/urbansci3030075 |
| OpenAlex | W2963005782 |
| Language | EN |
| References cited | 52 |
Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning
Analytics · Cartography · Census · Cluster analysis · Computer security · Confidentiality · Data mining · Data science · Environmental health · Geographic information system · Geography · Population · Public health · Spatial analysis · Computer Science · Data-Driven Disease Surveillance · Health disparities and outcomes · Medicine · Spatial and Panel Data Analysis
GeoDa
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The Child Opportunity Index
The Analysis of Spatial Association by Use of Distance Statistics
Socioeconomic Context and the Food Landscape in Texas
Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003–2015)
GIS as a Community Engagement Tool
Associations of Noise and Socioeconomic and -Demographic Status on Cardiovascular and Respiratory Diseases on Borough Level in a Large German City State
SNAPScapes
Mediating medical risk factors in the residential segregation and low birthweight relationship by race in New York City
Confidentiality Concerns with Mapping Survey Data in Reproductive Health Research
Spatial Point Pattern Analysis and Its Application in Geographical Epidemiology
Local Indicators of Spatial Association—Lisa
Assessing the applicability of GIS in a health and social care setting
| Citation velocity | historical |
|---|---|
| Highly cited | No |