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.

Productivity Hot Spots and Cold Spots: Setting Geographic Priorities for Achieving Food Production Targets

Rice is the most important staple food in the Philippines as it provides almost half of the calorie requirements of the population. The domestic supply of rice, however, is inadequate to meet local demand, making the Philippines one of the top five rice-importing countries in the world. To close the domestic supply-and-demand gap, agricultural planners and policymakers in the Philippines have been promoting interventions to increase farm-level productivity. In support of the government efforts to increase rice productivity, a stochastic frontier analysis combined with an optimized spatial hot spot analysis was conducted to determine the factors that may influence the increase in rice farm efficiency and also identify candidate areas for relevant geographic-specific rice production interventions. Results show that enhancing access to irrigation and farm inputs and increasing the producers' technical capacity can help address the local supply deficiency by potentially increasing yield per hectare to as high as 5.50 metric tons per hectare. The approach to potentially increase rice farm technical efficiency and productivity must consider the spatial nature of rice production as suggested by the findings from the optimized hot spot analysis. It is important that policy interventions consider areas with a high incidence of low levels of productivity and technical efficiency. These are the locations where agricultural planners and policymakers can make greater impacts on rice yields. Relevant policies and initiatives, therefore, should take into account the geographical location of farms to ensure the greatest contribution to attaining food production targets.

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan–Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan–Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi–Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014–2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi–Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000–5000 m, the slope range of 10–40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi–Ya’an section of the Sichuan–Tibet Railway.