Pedestrian safety is a top priority within the transportation planning community as cities promote sustainable transportation, alternative travel modes, and healthy lifestyles. If people feel unsafe while walking, they will choose other modes of transportation if they are able. To prioritize safety, it is important to know where pedestrian crashes are occurring and with what severity. Using spatial statistical methods including nearest neighbor index, Moran’s I, local indicators of spatial autocorrelation (LISA), and Getis-Ord G-statistic, this study seeks to analyze the pedestrian fatality locations within the state of Ohio over a 10-year period (2007–2016) to identify hot spots, cold spots, and spatial patterns across three different spatial scales: county, census tract, and traffic analysis zone (TAZ). It seeks to understand the effects of aggregated data across these spatial scales on the outcome of the analysis and determine the most useful spatial scale at which to study pedestrian fatalities. The study concludes that spatial analyses at small scales are most informative. It goes on to recommend locations within Ohio for further analysis based on the resulting maps, including areas with outliers. As of writing this, there is no current statewide pedestrian fatality analysis for the state of Ohio.

行人安全是交通规划社区中的头等大事，因为城市提倡可持续交通，替代性出行方式和健康的生活方式。如果人们在行走时感到不安全，则将尽可能选择其他运输方式。为了优先考虑安全，重要的是要知道发生行人交通事故的地点和严重程度。使用空间统计方法，包括最近邻居指数，Moran's I，空间自相关的局部指标（LISA）和Getis-Ord G统计量，本研究旨在分析10年内（2007年至2016年）俄亥俄州的行人死亡地点，以识别热点，热点，以及跨三个不同空间尺度的空间格局：县，人口普查区和交通分析区（TAZ）。它试图了解跨这些空间尺度的汇总数据对分析结果的影响，并确定研究行人死亡的最有用的空间尺度。该研究得出的结论是，小规模的空间分析最有用。它会根据生成的地图（包括具有异常值的区域），推荐俄亥俄州内的位置，以进行进一步分析。截至撰写本文时，目前尚无针对俄亥俄州的全州行人死亡率分析。