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Enhancing Lidar Data Integrity in the Coastal Everglades
Published in Caiyun Zhang, Multi-sensor System Applications in the Everglades Ecosystem, 2020
For comparison with the object-based lidar-DEM generated from the machine learning prediction, interpolated DEMs were generated for this study. Estimating the elevation of unknown lidar locations as a function of nearby lidar ground return measurements may be thought of as an interpolation problem where the output is a continuous variable (Figure 16.5). All spatial interpolation approaches are relevant to the first law of geography where “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970). In spatial analysis, this is referred to as spatial autocorrelation or evidence that data located near one another are likely to be more similar than data located further away from one another (O’Sullivan and Unwin, 2010). For example, elevation for an Everglades tree island will be more similar than elevation in the surrounding marsh (Figure 16.5). Therefore, many approaches to spatial interpolation apply weights to existing observations based on their proximities to the unsampled locations being estimated. Near points generally receive higher weights than points that are far away. Empirical Bayesian Kriging (EBK), an automatic geostatistical interpolator, produces near-accurate estimations along with their uncertainties based on statistical models that include autocorrelation. Here we applied the EBK interpolator to generate a lidar-DEM L for comparison purposes.
Spatial analysis and modelling
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Spatial analysis refers to the process of (and set of techniques used for) acquiring, analysing and presenting spatial data. Spatial modelling refers to the analytical process of representing spatial data (cartographic models, network models, spatio-temporal models or optimisation models, for example). Spatial analysis and modelling can also be referred to as spatial data analysis (Lloyd, 2010; Haining, 2003) or spatial analysis modelling (Patterson, 2016). Some organisations also use the term ‘geospatial analysis’ when referring to spatial analysis (this can be seen on some of the websites that provide spatial analysis tools and software, listed below). However, ‘spatial’ relates to the position, area, shape and size of places, spaces, features, objects or phenomena, whereas ‘geospatial’ relates to data that contain a specific geographic component (locational information). This chapter discusses spatial analysis and modelling and Chapter 21 discusses geospatial analysis, along with methods and techniques such as locational analysis, distance analysis, surface analysis and geovisualisation.
Impact of Noise Pollution on Human Health in Barasat Urban Area, West Bengal
Published in Uday Chatterjee, Arindam Biswas, Jenia Mukherjee, Dinabandhu Mahata, Sustainable Urbanism in Developing Countries, 2022
Kasturi Mukherjee, Nandita Deb, Abira Dutta Roy, Pratik Dash
The use of GIS technology facilitates noise pollution study in a number of ways. It enables various spatial databases to be created, encompassing essential information such as settlements, roads, locations of schools and hospitals, and spatial coverage of land-use types on the one hand, while providing opportunities for data processing, spatial analysis and visualization on the other. The collection, creation and integration of spatial data has become essential in various research domains and is now easy and cost-effective in GIS platforms. However, sophistication in GIS technology provides new opportunities to explore the various aspects of GIS modeling pertain to noise pollution for better decision making in urban planning.
GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques
Published in International Journal of Injury Control and Safety Promotion, 2021
Amir Mohammadian Amiri, Navid Nadimi, Vahid Khalifeh, Moe Shams
Hotspot mapping techniques can be categorized into three major groups: spatial analysis methods, interpolation methods and mapping cluster methods. Spatial analysis is the process of analysing attributes, locations and connections in spatial data, which can provide various valuable insights. This method includes several subcategories, such as kernel density estimation (KDE), point density estimation (PDE) and line density estimation (LDE). The second approach, interpolation, is the approximate judgment of surface values at the unknown points using the surface values of surrounding better-known points. Inverse distance weighting (IDW), Kriging, spline, and natural neighbour are the most well-known interpolation techniques. The third method of hotspot mapping, mapping cluster, is defined as the degree to which a set of spatial feature and the data values associated with it are applied. Average nearest neighbour, Getis-Ord (Gi*) and Moran’s I are among the most famous mapping clusters (Chainey et al., 2008).
Clustering and pedestrian crashes prediction modelling: Amman case
Published in International Journal of Injury Control and Safety Promotion, 2023
If any were eliminated, it was done by using the related topology rules in ArcGIS software – the tool used to develop the primary GIS layers and define the descriptive attributes. Crash data coordinates define the spatial dimension of data. The crash layer was joined with TAZ, and crashes were grouped and allocated to the related TAZs’ centroid. Demographic data, road network extents, and surrounding land-use codes were collected and analysed concerning road crashes. Three spatial analysis methods were used: spatial autocorrelation (Moran’s I), cluster K-Means, and spatial regression. GeoDa 0.9.5 software was used to analyse spatial measures; it is freely downloadable and has a user-friendly interface.