The analysis pattern can be used to evaluate the spatial patterns of a set of elements as clustered, dispersed, or random. Analyzing patterns employs inferential statistics by establishing a null hypothesis in advance during statistical testing, assuming that the elements or their associated values exhibit spatial randomness. The analysis results provide a p-value indicating the probability of the null hypothesis being true, which helps determine whether to accept or reject the null hypothesis. The results also include a Z-score representing the standard deviation multiple, used to identify clustering, dispersion, or randomness in the data. Calculating probabilities becomes crucial when making decisions requiring high confidence levels. For instance, statistical evidence may be necessary to justify decisions involving public safety or legal matters.
Analyzing patterns quantifies data patterns through initial analysis before conducting deeper investigations. It provides four analytical tools: Spatial Autocorrelation, High/Low Clustering, Incremental Spatial Autocorrelation, and Average Nearest Neighbor, with specific descriptions as follows:
- Spatial Autocorrelation: Measures spatial autocorrelation using Moran's I statistic based on element locations and attribute values.
- High/Low Clustering: Quantifies the clustering degree of high or low values.
- Incremental Spatial Autocorrelation: Measures spatial autocorrelation across distance intervals and optionally generates line charts showing distances with corresponding Z-scores. Significant peak Z-scores indicate optimal clustering distances, often used as parameter values for tools requiring "distance band" or "radius" settings.
- Average Nearest Neighbor: Calculates the nearest neighbor index based on average measured distances between each element and its closest neighbor.
Related Topics
Measuring Geographic Distributions