Spatial Data Mining Approaches for GIS – A Brief Review

This study aims to find spatial clusters of diabetes and physical inactivity among a sample population in Kerala, India, and evaluate built environment characteristics within the high and low spatial clusters. Spatial clusters with a higher and lower likelihood of diabetes and physical inactivity were identified using spatial scan statistic at various radii. Built environment characteristics were captured at panchayat level and 1600 m buffer around participant location using Geographical Information Systems. Comparison of sociodemographic and built environment factors was carried out for participants within high and low spatial clusters using t tests. Ten high and 8 low spatial clusters of diabetes and 17 high and 23 low spatial clusters of physical inactivity were identified in urban and rural areas of Kerala. Significant differences in built environment characteristics were consistent for low spatial clusters of diabetes and physical inactivity in the urban scenario. Built environment characteristics were found to be relevant in both urban and rural areas of Kerala. There is an urgent call to explore spatial clustering of non-communicable diseases in Kerala and undo the one-size-fits-all approach for prevention and control of non-communicable diseases.

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“…20 The road network was captured using OpenStreetMap where “highway” or “walkway” had true value. 21…”

Section: Methodsmentioning

confidence: 99%

“…20 The road network was captured using OpenStreetMap where "highway" or "walkway" had true value. 21 Panchayat-level characteristics. The following variables were captured from the panchayat/ municipality that the participants belonged to:…”

Section: Built Environment Factorsmentioning

confidence: 99%

Spatial Clusters of Diabetes and Physical Inactivity: Do Neighborhood Characteristics in High and Low Clusters Differ?

Valson

Kutty

Soman

et al. 2019

Asia Pac J Public Health

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“…With respect to the second area, data analysis, we are particularly interested in spatial analysis. Spatial data mining (SDM) can be defined as the extension of data mining for extracting implicit knowledge over spatial information [12]. This extension considers the inherent features of geographic information systems (GIS) with respect to the way of representing spatial data and their operations.…”

Section: Related Workmentioning

confidence: 99%

Experiences from a Data Analysis of Crimes against Humanity

Buccella

Manrique

Troncoso

et al. 2021

JCS&T

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Data analysis is a widely researched field, where innumerable applications allow to discover domain particularities that are specially useful. In this paper, we introduce the data analysis process that we applied to two different systems storing information about statements and testimonies of crimes against Humanity. We describe the activities, design decisions and lessons learned from implementing a specific goal, which involves transforming text data into georeferenced information.

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“…It is expected to integrate various heterogeneous data sources [31] to serve as geographical-related analysis. For example, to know the population density of an urban region, the corresponding data in a specified area must be prepared according to the geographical scope and time span in the national population database.…”

Section: Association Analysismentioning

confidence: 99%

Data Association at the Level of Narrative Plots to Support Analysis of Spatiotemporal Evolvement of Conflict: A Case Study in Nigeria

Han

Tian

et al. 2016

IJGI

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Abstract:Open data sources regarding conflicts are increasingly enriched by broad social media; these yield a volume of information that exceeds our process capabilities. One of the critical factors is that knowledge extraction from mixed data formats requires systematic, sophisticated modeling. Here, we propose using text mining modeling tools for building associations of heterogeneous semi-structured data to enhance decision-making. Using narrative plots, text representation, and cluster analysis, we provide a data association framework that can mine spatiotemporal data that occur in similar contexts. The framework contains the following steps: (1) a novel text representation is presented to vectorize the textual semantics by learning both co-word features and word orders in a unified form; (2) text clustering technology is employed to associate events of interest with similar events in historical logs, based solely on narrative plots of the events; and (3) the inferred activity procedure is visualized via an evolving spatiotemporal map through the Kriging algorithm. Our results demonstrate that the approach enables deeper discrimination into the trends underlying conflicts and possesses a narrative reasoning forward prediction with a precision of 0.4817, in addition to a high consistency with the conclusions of existing studies.

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Spatial Data Mining Approaches for GIS – A Brief Review

Cited by 30 publications

References 70 publications

Spatial Clusters of Diabetes and Physical Inactivity: Do Neighborhood Characteristics in High and Low Clusters Differ?

Spatial Clusters of Diabetes and Physical Inactivity: Do Neighborhood Characteristics in High and Low Clusters Differ?

Experiences from a Data Analysis of Crimes against Humanity

Data Association at the Level of Narrative Plots to Support Analysis of Spatiotemporal Evolvement of Conflict: A Case Study in Nigeria

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