{'fraction_non_alphanumeric': 0.024051803885291396, 'fraction_numerical': 0.0, 'mean_word_length': 5.848101265822785, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Extending Getis–Ord Statistics to Account for Local Space–Time Autocorrelation in Spatial Panel Data

Space and time are both crucial characteristic dimensions of geographic events and phenomena. Although exploratory spatial data analysis (ESDA) can be used to visualize and summarize complex spatial patterns, it has limitations in capturing the temporal dynamics of geographic features. Efforts have been made to incorporate the time dimension into ESDA techniques to detect space–time clustering or trends. Localized space–time statistics that could help in exploratory space–time data analysis (ESTDA), however, are still lacking. Focusing on spatial panel data, our work extended Getis–Ord and statistics using a space–time contemporaneous weight matrix and a space–time lagged weight matrix to account for local space–time autocorrelation. Two applications in this article show that the newly developed method can be used to summarize space–time patterns from spatial panel data, identify changes of landscape more consistently, and lend the results readily to visualization.