Study on Monitoring Water Area in Irrigation Area by Local Space Self-correlation Index

Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z>1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.

SPATIAL ANALYSIS OF CRIME HOT-SPOT IN THE NORTHEAST PENANG ISLAND DISTRICT AND KUCHING DISTRICT, MALAYSIA

Over time, the relation between criminal acts with drug abuse cases has been discussed pedantically. From social and spatial points of view, this paper aims to determine the hot spot areas of burglary cases in the Northeast Penang Island District and Kuching District. The gained results of burglary cases are then being correlated with the presence of drug abuse cases. Both study areas came with location coordinates of the incident based on police stations boundaries and police station sector boundaries from the year 2015. The type of analysis used for this research is Optimized Hot Spot Analysis. Results for burglary cases of both areas are divided into two (2) which are daytime and nighttime. The spatial analysis revealed that there are five (5) sectors identified as hot spots for the Northeast Penang Island District which involve Jelutong Police Station boundary and Ayer Itam Police Station boundary, while none of the areas identified as hot spot areas in Kuching District.

Cambios residenciales y migratorios de la población extranjera en la ciudad de Valencia (2014-2017)

Desde comienzos de siglo se ha producido en las ciudades españolas un importante aumento de la entrada de población extranjera que ha transformado el paisaje social de la ciudad, dando lugar en algunos casos a concentraciones espaciales de este tipo de población en áreas concretas. En este artículo se analizan las tendencias actuales (post-crisis) de localización de la población extranjera en la ciudad central metropolitana, utilizando como caso de estudio la ciudad de Valencia. Para ello se estudian los cambios residenciales y migratorios a escala de sección censal de la población extranjera clasificada por nacionalidad y nivel educativo, aplicando herramientas estadísticas (I Global de Moran y Hot-Spot Analysis) que permiten identificar las tendencias espaciales significativas. Los resultados indican que la movilidad residencial y migratoria de la población extranjera está contribuyendo, en líneas generales, a la consolidación y ampliación geográfica del patrón territorial existente. Sin embargo, se detectan algunos cambios debidos a la emergencia de nuevos espacios atractivos para la población extranjera de nivel educativo más elevado, especialmente nacionales de países de la Unión Europea, en entornos específicos que han sido revitalizados por proyectos urbanísticos concretos o por procesos de renovación urbana más amplios.

Using Emerging hot Spot Analysis to Explore Spatiotemporal Patterns of Housing Vacancy in Ohio Metropolitan Statistical Areas

We highlight the use of a newer method—emerging hot spot analysis of space-time cubes from defined locations—for examining the spread of housing vacancy in large, Ohio MSAs. Using this method, we discovered that many Ohio MSAs concurrently experienced spread, contraction, and vacancy stabilization in census tracts located adjacent to, or within close proximity of, one another. These results indicate that vacancy proliferation is not solely a matter of geographic determinism, whereby high vacancy in one tract predicts high vacancy in neighboring tracts in future years. We also found that vacancy spread at the tract level is associated with population dynamics at the neighborhood, city, and MSA levels. Our findings suggest that vacancy reduction initiatives should account for population trends at various geographic scales, not just physical conditions within a particular neighborhood or tract.

1011. Geospatial Analysis of Antibiotic Susceptibility in Wisconsin

Background The global threat of antimicrobial resistance (AMR) varies regionally. Regional differences may be related to socio-economic factors such as the Area Deprivation Index (ADI) score. Our hypothesis is that AMR spatial distribution is not random. Methods Patient level antibiotic susceptibility data was collected from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). Patient addresses were geocoded to coordinates and joined with US Census Block Groups. For each culture source, we included the initial E. coli isolate per patient per year with a patient address in Wisconsin. Percent susceptibility was calculated by block group. Spatial autocorrelation was determined by Global Moran’s I, which quantifies the attribute being analyzed as spatially dispersed, randomly distributed, or clustered by a range of −1 to +1. Linear regression correlated ADI to susceptibility. Hot spot analysis identified blocks with statistically significant higher and lower susceptibility (Figure 1). Figure 1. Geographic example of hot spot analysis and interpretation. Results The UW Health results included more urban areas, more block groups and greater isolate geographic density (n = 44,629 E. coli, 2009-2018), compared to Fort HealthCare (n = 6,065 isolates, 2012-2018) and MCHS (50,405 isolates, 2009-2018). A positive spatially clustered pattern was identified from the UW Health data for ciprofloxacin (Moran’s I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole (TMP/SMX) susceptibility (Moran’s I = 0.180, p < 0.001; Figures 2-3). Fort HealthCare and MCHS distribution was likely random for TMP/SMX and ciprofloxacin by Moran’s I. Linear regression of ADI (scale 1-10, least to most disadvantaged) and susceptibility did not find significance, but susceptibility was lower in more disadvantaged block groups. At the local level, we identified hot and cold spots with 90%, 95%, and 99% confidence, with more hot spots in rural regions. Figure 2. Results from Moran’s Index analysis identifying geographically clustered ciprofloxacin susceptibility results. Figure 3. Results from Moran’s Index analysis identifying geographically clustered sulfamethoxazole/trimethoprim susceptibility results. Conclusion Overall, Moran’s I analysis is more able to identify a clustered pattern in urban versus rural areas. Yet, the local hot spot results indicate that variations in antibiotic susceptibility may be more common in rural areas. The results are limited to data from patients with access to the health systems included. Disclosures Warren Rose, PharmD, MPH, Merck (Grant/Research Support)Paratek (Grant/Research Support, Advisor or Review Panel member)