标题： 埃塞俄比亚德赛市基于地理空间技术的犯罪热点分析与制图 显示英文标题

标题： Crime Hotspot Analysis and Mapping Using Geospatial Technology in Dessie City, Ethiopia

摘要： 在过去几十年里，许多国家的犯罪率和青少年犯罪率急剧上升；然而，需要注意的是，犯罪趋势可能因地理区域而异。 本研究的主要目标是利用地理技术来绘制和分析德西市的犯罪模式。 为了调查犯罪的地理聚集情况，研究人员使用了半变异函数建模和莫兰指数（Moran's I）的空间自相关分析。 根据统计学上显著的高Z分数（从0.037到4.608），德西市中心城区的Hote、Arada和Segno社区被发现是犯罪频发的“热点”位置。 另一方面，Menafesha和Bounbouwha两个郊区低负Z分数（从-3.231到-0.116）表明这些地区是犯罪的“冷点”集中区。 整体分析显示了一个显著的正向空间自相关性，其指标为0.027492，Z分数为3.297616（p<0.01），这表明德西市的犯罪呈现集群模式。 除谋杀外，大多数犯罪表现出南北方向性，而谋杀则从东北向西南发展。 所有犯罪类型的平均中心位于Hote中部地区。 为了应对德西市及其他发展中大都市日益增长的犯罪率这一复杂问题，可以通过地理空间分析获得的知识来制定更聚焦且高效的执法技术和资源部署策略。 显示英文摘要

摘要： Over the past few decades, crime and delinquency rates have increased drastically in many countries; nevertheless, it is important to note that crime trends can differ significantly by geographic region. This study's primary goal was to use geographic technology to map and analyze Dessie City's crime patterns. To investigate the geographic clustering of crime, the researchers used semivariogram modeling and spatial autocorrelation analysis with Moran'sI. The neighborhoods of Hote, Arada, and Segno in Dessie's central city were found to be crime-prone "hot spot" locations, as evidenced by statistically significant high Z-scores ranging from 0.037 to 4.608. On the other hand, low negative Z-scores ranging from -3.231 to -0.116 indicated "cold spot" concentrations of crime in the city's north-central sub-cities of Menafesha and Bounbouwha. With an index of 0.027492 and a Z-score of 3.297616 (p<0.01), the analysis overall showed a substantial positive spatial autocorrelation, suggesting a clustered pattern of crime in Dessie. The majority of crimes showed a north-south directionality, except for murder, which trended from northeast to southwest. The mean center of all crime types was found in the central Hote area. To address the complicated problem of rising crime rates in Dessie and other developing metropolitan areas, more focused and efficient enforcement techniques, and resource deployment can be informed through the knowledge acquired from the geospatial analysis.