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Tuberculosis (TB) is one of the tenth highest causes of death in Indonesia and even worldwide. Tuberculosis is an infectious disease caused by an infection of Mycobacterium tuberculosis bacterium. Kebumen is one of the districts with high tuberculosis cases and tends to increase every year. Based on the high case number, it is necessary to start research that examines patterns of spread. Spatial analysis is a very useful tool to evaluate the spreading pattern of the tuberculosis disease according to its geographical location. The study aimed to spatially analyze tuberculosis spread pattern from 2016 to 2018 using the spatial autocorrelation method through Moran Index and Local Indicator of Spatial Association (LISA). The study showed that the spatial autocorrelation in the spreading patterns of tuberculosis occurred in Kebumen and had a clustered pattern because of Moran Index is positive. The results of the LISA analysis in the High-High quadrant showed that the high tuberculosis cases correlated with areas that also had high tuberculosis cases. Sixteen villages were included in the High-High quadrant.

This study aims at better understanding the geospatial phenomenon of offenses or violations happening at the urban pedestrian with a particular focus on (a) motorcyclists passing through pedestrian lanes, (b) street vendors selling at pedestrian lanes, and (c) illegal parking at pedestrian lanes during and post new policy on pedestrian infrastructure. The first and the second type of offense involves dynamic behaviors while the third offense inherently involves both behaviors. Tweets and Qlue reports (represent passive and active crowdsourced data) regarding Tanah Abang pedestrian condition are employed as the materials of the study case. Based upon spatial and temporal values of crowdsourced data, statistics charts and heatmap visualization are presented to know the spatial pattern and fluctuation of offenses. The results of this study are presented as a collection of visual displays in order to provide geospatial insights such as dynamics of reports on top of an online map using Leaflet. The study illustrates the usefulness of geospatial analytics to correlate offenses extracted from crowdsourced data with pedestrian safety as a new policy on pedestrian infrastructure was introduced.