ObjectivesXinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions.MethodsNumbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran’s I statistic. Anselin’s local Moran’s I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis.ResultsIncidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p

Title of reference article:Nine Days of Naptown Arrests: How and Why Spatial Data Should Discomfort UsAuthor J. Kevin ByrneDate authored: August 23, 2020Abstract: During nine successive days in 2019 Indianapolis (IN) police made arrests across six districts. Exploratory spatial data analysis (ESDA) revealed how variables of arrests, race, aggressive use of force (UOF), injuries, and their location interact with each other. Scatterplots with R-squared values > 0.6 suggested aggressive UOF contributed to injuries of arrested residents across all races, Caucasian officers may have excessively injured arrested residents, and aggressive UOF correlated with arrests of African-Americans. Findings for parallel-coordinate-plots dove deeper in terms of spatial implications and ethical considerations (e.g., by visually demonstrating presence of a cluster of observed residents’ arrests as coinciding with African-American census geodemographics). This “small-sample” can surprise the reader. My conclusion proposed two aims: 1) solidify hypotheses (for further ESDA) that may induce ethical discomfort (a good thing) pertaining to the subject of structural racism, and 2) use findings to usher civic policymakers down more strident paths to sociocultural change.Indianapolis (IN) police districts and zones shapefiles that were made public by ESRI were used by way of my ESDA. Path to shapefiles’ source:http://data.indy.gov/datasets/indianapolis-police-zonesN.B.: Safari web-browser not recommended. Shapefile metadata are here: https://www.arcgis.com/home/item.html?id=b59421675f2a40fda9b00beeb875996fUsing GeoDa I did a spatial join that permitted my ESDA to analyze variables with scatterplots, PCPs, and datamaps. My final GeoDa file – titled NapWorksProj.gda – is herewith.Also herewith are my GeoDa's shapefiles – created natively – titled as follows:· NapWorks.cpg· NapWorks.dbf· NapWorks.prj· NapWorks.shp· NapWorks.shx

In this blog I’ll share the workflow and tools used in the GIS part of this analysis. To understand where crashes are occurring, first the dataset had to be mapped. The software of choice in this instance was ArcGIS, though most of the analysis could have been done using QGIS. Heat maps are all the rage, and if you want to make simple heat maps for free and you appreciate good documentation, I recommend the QGIS Heatmap plugin. There are also some great tools in the free open-source program GeoDa for spatial statistics.