Background
The outbreak of the novel coronavirus (COVID-19) has rapidly spread, causing million confirmed cases, thousands of deaths, and economic losses. The number of cases of COVID-19 in Jakarta is the largest in Indonesia. Furthermore, Jakarta is the capital city of Indonesia which has the densest population in the country. There is need for geospatial analysis to evaluate the demand in contrast to the capacity of Referral Hospitals and to model the spreading case of Covid-19 in order to support and organize an effective health service.
Methods
We used the data from local government publicity for COVID-19 as trusted available sources. By using the verifiable data by observation from the local government, we estimated the spatial pattern of distribution of cases to estimate the growing cases. We performed service area and Origin-Destination (OD) Cost Matrix in support to existing referral hospital, and to create Standard Deviational Ellipse (SDE) model to determine the spatial distribution of COVID-19.
Results
We identified more than 12.4 million people (86.7%) based on distance-based service area, live in the well served area of the referral hospital. A total 2637 positive-infected cases were identified and highly concentrated in West Jakarta (1096 cases). The results of OD cost matrix in a range of 10 km show a total 908 unassigned cases from 24 patient’s centroid which was highly concentrated in West Jakarta.
Conclusions
Our results indicate the needs for additional referral hospitals specializing in the treatment of COVID-19 and spatial illustration map of the growth of COVID-19′ case in support to the implementation of social distancing in Jakarta.
This research to analyse the pattern of rice field productivity that is identified through landscape perspective. Identification of productivity pattern has been done partially based on each typology of land components into several segment of the Citarum watershed, West Java Province, Indonesia. Spatial autocorrelation through GIS tool is used as the method in this research. By using moran’s I (index) measurement, degree of dependency of these variables are generated to find the spatial pattern. The result of this study is separated the value of productivity based on segments of watershed, the values of the average of productivity are upstream (6,39 ton/Ha), middle stream (6,52 ton/Ha), and downstream (7,17 ton/Ha), sequentially. The highest productivity is in the downstream area (9,83 ton/Ha) and the lowest is in the upstream area (4,55 ton/Ha). In accordance with physiographic typology showed the rice field in the middle stream has more variation than the upstream or teh downstream area. The highest of average rice field productivity is on alluvial plain. Overall, the rice field productivity on the hills is higher rather that other types of landform the stuctural formation is more dominant, in addition. The spatial pattern shows the distribution of rice field productivity most likely to clustered based on the similarity of physiographic type. Statistically, it has p-value <0,01 and z-score >2,58 (239,26) correspond to Spatial Autocorrelation (Moron’s I). This positive value means a less than 1% likelihood that this clustered pattern could be result of random choice, which the rice field productivity value has similar pattern to others. Thus, it can be generated that the pattern of rice field productivity has a very close relation with the physical characteristics which associated of each typology of land components.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.