Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods
Bibliographic Data
| ID | 15515257 |
|---|---|
| Authors | D W S Wong (0000-0002-0525-0071, George Mason University, corresponding author) |
| Year | 2021 |
| Volume | 18 |
| Issue | 18 |
| Pages | 9848-9848 |
| Publication date | 2021-09-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph18189848 |
| PMID | 34574771 |
| OpenAlex | W3198927044 |
| Language | EN |
| Citations received | 3 |
| References cited | 30 |
Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable
Cluster (spacecraft · Cold spot · Data mining · Geography · Heuristic · Hot spot (computer programming · Spatial analysis · Statistic · Statistics · Computer Science · Data-Driven Disease Surveillance · Economic and Environmental Valuation · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence
Applied Spatial Statistics for Public Health Data
A spatial scan statistic
Patterns and causes of uncertainty in the American Community Survey
The Analysis of Spatial Association by Use of Distance Statistics
Measuring Local Spatial Autocorrelation with Data Reliability Information
The Spatial Patterning of County Homicide Rates
The Influence of Socioeconomic and Environmental Determinants on Health and Obesity
Perceived Barriers to Physical Activity According to Stage of Change and Body Mass Index in the West Virginia Wisewoman Population
Incorporating Data Quality Information in Mapping American Community Survey Data
Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980-1995
Estimating the probability of local crime clusters
Conceptual and practical issues in the detection of local disease clusters
Local Indicators of Spatial Association—Lisa
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,75 |
| Citation span | 2022 - 2025 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |