The emergence of 2019 novel corona virus disease (COVID-19) raised global health concerns throughout the world. It has become a major challenge for healthcare personnel and researchers throughout the world to efficiently track and prevent the transmission of this virus. In this paper, the role of geographic information system (GIS) based spatial models for tracking the spread of COVID-19 and discovery of testing centres in Maharashtra, India was studied. The datasets collected from diverse sources were geocoded to make it geospatially compatible. A three-tiered framework was proposed to practically realize the impact of COVID-19 in a cartographic fashion. Initially, choropleth maps labeled with testing centres, number of confirmed cases and casualties were visualized in a district-wise manner. Heatmaps for visualizing the spatial density of confirmed cases and casualties were presented. The visualization of spatial K-means clustering for optimal value of “k” estimated using the heuristic-based Elbow method was provided along with zonal analysis of the districts. Map showing spatial autocorrelation was also presented to identify spatial hotspots and coldspots. The districts of Pune and Thane reported respective $z-$scores of 3.424 and 3.347 along with $p-$values of 0.006 and 0.001 respectively. It was inferred from the generated results that Pune and Thane districts in Maharashtra were identified as COVID-19 hotspots. Based upon this analysis, certain effective mitigation strategies can be devised in order to check the uncontrolled spread of COVID-19 in the identified hotspot areas.

2019 年新型冠状病毒病 (COVID-19) 的出现引起了全世界的全球健康关注。有效追踪和预防这种病毒的传播已成为全世界医护人员和研究人员面临的重大挑战。在本文中，研究了基于地理信息系统 (GIS) 的空间模型在跟踪 COVID-19 的传播和发现印度马哈拉施特拉邦检测中心方面的作用。从不同来源收集的数据集进行了地理编码，以使其在地理空间上兼容。提出了一个三层框架，以便以制图方式实际实现 COVID-19 的影响。最初，标有检测中心、确诊病例数和伤亡人数的等值线图以区域方式可视化。展示了用于可视化确诊病例和伤亡的空间密度的热图。提供了空间 K 均值聚类的可视化，用于使用基于启发式的 Elbow 方法估计的“k”的最佳值以及区域的区域分析。还提供了显示空间自相关的地图，以识别空间热点和冷点。浦那区和塔那区分别报告了$z-$3.424 和 3.347 的分数以及 $p-$值分别为 0.006 和 0.001。根据生成的结果推断，马哈拉施特拉邦的浦那和塔纳地区被确定为 COVID-19 热点。基于此分析，可以设计某些有效的缓解策略，以检查 COVID-19 在已识别的热点区域中不受控制的传播。