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Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark
Published in Qurban A. Memon, Shakeel Ahmed Khoja, Data Science, 2019
S. Vasavi, P. Vamsi Krishna, Anu A. Gokhale
Every location on this globe is a representation of intersection of latitude and longitude coordinates. Spatial operations are classified by the authors of [1]: proximity analysis queries such as “Which parcels are within 100 meters of the subway?,” contiguity analysis queries such as “Which states share a border with Coorg?,” and neighborhood analysis queries such as “Calculate an output value at a location from the values at nearby locations?” Geohashing techniques are used to find the nearest location of a specified source location with high precision values. Latitude and longitude are used to calculate geohash value: the higher the precision value, the more the length of geohash value. Instead of sequential processing of the database, a parallel search process must be done for a required destination using this geohash. To achieve this, MongoDB is integrated with Spark, which is an efficient distributed parallel processing.
GIS and Transportation
Published in Dušan Teodorović, The Routledge Handbook of Transportation, 2015
The ability to carry out analysis has been an important asset of GIS. Analytical methods range greatly, extending structured/standard query language (SQL) based query found in DBMS. In particular, spatial information gives rise to a need for geographic query. Thus, GIS supports identifying objects in a layer that are near or far away from features or other objects, or in a user defined area(s). As information is spatially based, mapping and geovisualization are essential, so GIS supports this in various ways. Of course, these methods and more must be supported by spatial analytical operations of all sorts. Given this, standard GIS functionality includes analysis approaches such as proximity, buffer, overlay, and map algebra, among others. Proximity analysis includes identification of neighbors as well as deriving distance between objects. Buffer analysis is the ability to derive proximity zones/areas around objects. Overlay has two interpretations, depending on the spatial data model. For vector GIS, overlay refers to the spatial combination of two or more vector object layers, creating a new layer that is all of the smallest unique object that results from geometric unification of multiple layers (note as well that resolution of the attributes for newly created objects is also necessary, often through the use of an areal interpolation process). Finally, map algebra (also equivalent to raster overlay) is the process of taking one or more input layers and creating an output layer of the same spatial resolution with cell attribute values that are a function of the input layers. Details of these approaches (and more) can be found in Longley et al. (2011) and Church and Murray (2009), among others.
Geospatial modeling of the tropical cyclone risk in the Guangdong Province, China
Published in Geomatics, Natural Hazards and Risk, 2021
Mindan Zhou, Yaoqiu Kuang, Zhu Ruan, Mengyi Xie
Vulnerability is the susceptibility of an element to environmental change or the extent to which a community and environment may be affected by a particular hazard (Rashid 2013; UNISDR 2009). In total, nine socio-economic and environmental factors, including the slope angle, NDVI, drainage density, proximity to highways, proximity to railways, proximity to the coastline, population density, GDP, land use and land cover were used to visualize the vulnerability across the Guangdong Province (Figure 4). The slope angle, NDVI, and river density are important measures of physical vulnerability: A lower slope angle, lower vegetation coverage, and high river density suggest a high vulnerability to TC hazards. Population density, GDP, and land use and cover correspond to socio-economic vulnerability. The layer of slope is calculated from the Digital Elevation Model (DEM) using slope analysis. The layers of population density, GDP, and NDVI were extracted from spatial grid datasets of the Chinese population, GDP, and vegetation index based on a 1 km spatial resolution. Land use and cover were extracted from multi-period land use and land cover remote sensing datasets in China (CNLUCC). Furthermore, proximity to roads, railways, and coastline are important indicators of the susceptibility of exposed elements: The closer to the roads, railways, and coastline, the higher the impact. To produce the layers of proximity to coastline, road, railways, and drainage density, proximity analysis was performed using ArcGIS software.
Analysis of exposure to vector-borne diseases due to flood duration, for a more complete flood hazard assessment: Llanos de Moxos, Bolivia
Published in Ribagua, 2018
Vladimir Moya Quiroga Gomez, Shuichi Kure, Keiko Udo, Akira Mano
In summary, the present flood duration hazard criteria depend on two unknown parameters for defining mosquito-breeding pools: MFD and MFDO. Based on the mentioned criteria, this study analyzed the performance of the possible MFD and MFDO values; and considered two MFD values (seven and ten days) and two MFDO values (0.25 and 0.50 m). Besides, it is important to consider that mosquito will fly some distance to feed. Mosquito behavior analysis depends on several factors, such as wind direction, wind speed, number of feeds per day, population and others. Such data is not available. Besides, the present study aims to analyze the areas exposed to mosquitos (not the mosquito dynamics). Thus, a proximity analysis using GIS was performed for defining the areas exposed to mosquito, considering mosquito flying distances. The study used the proximity analysis tool from QGIS. In the present study, a distance of 3500 m was assumed, because the Aedes and Anopheles mosquitoes have an average MFD of 3500 m (Verdonschot & Besse-lototskaya, 2014). Areas located closer than 3.5 km from the mosquito breeding pools were considered as high hazard zones; however, some studies report that under some circumstances, mosquitos may fly up to 10 km (Bogojevic, Merdic, & Bogdanovic, 2011). Thus, areas located closer than 10 km from the mosquito breeding pools were considered as low hazard zones. Figure 4(b) shows the flood duration hazard as a combination of flood depth, flood duration and distance. Table 1 summarizes the data used in the study.