Proximity Analysis PDF

Spatial analysis - Spatial analysis is the process of examining attributes, locations, and relationships between features of spatial data. It uses analytics, computational models, and algorithms - This method transforms raw data into actionable information by analyzing geographic f...

Spatial analysis - Spatial analysis is the process of examining attributes, locations, and relationships between features of spatial data. It uses analytics, computational models, and algorithms - This method transforms raw data into actionable information by analyzing geographic features collected through satellites, maps, and other sources.  Overlay - Before the days of GIS, cartographers would create maps on clear plastic sheets and overlay these sheets on a light table to create a new map of the overlaid data. - Overlay is a powerful analytical technique used to combine multiple spatial datasets to identify relationships between them. This process involves layering different types of geographic information, such as land use, soil types, or flood zones, to create a new dataset that contains combined information from the original layers. Overview: - Placing map layers on top of each other - Looking at multiple map layers and selecting objects from one layer that lie within an object from another layer - Conduct overlay operations by combining layers of data to create one new layer Method: there are two methods for performing overlay analysis---feature overlay (overlaying points, lines, or polygons) and raster overlay. 1. Feature Overlay - The key elements in feature overlay are the input layer, the overlay layer, and the output layer. - The overlay function splits features in the input layer where they are overlapped by features in the overlay layer. - New areas are created where polygons intersect. - If the input layer contains lines, the lines are split where polygons cross them. - These new features are stored in the output layer---the original input layer is not modified. - The attributes of features in the overlay layer are assigned to the appropriate new features in the output layer, along with the original attributes from the input layer. - Overlay toolset contains tools to overlay multiple feature classes to combine, erase, modify, or update spatial features, resulting in a new feature class. New information is created when overlaying one set of features with another. All of the overlay operations involve joining two sets of features into a single set of features to identify spatial relationships between the input features - Overlay operations can include various tools and methods, such as: - **Intersect**: Combines features that overlap in all layers. - **Union**: Combines all features from all layers. - **Erase**: Removes features that overlap with the erase layer. - **Spatial Join**: Joins attributes from one layer to another based on spatial relationships 2. Raster Overlay - In raster overlay, each cell of each layer references the same geographic location. - That makes it well suited to combining characteristics for numerous layers into a single layer. - Usually, numeric values are assigned to each characteristic, allowing you to mathematically combine the layers and assign a new value to each cell in the output layer - Overlay analysis to find locations meeting certain criteria is often best done using raster overlay  Thematic overlays  - refer to the process of superimposing multiple thematic maps to analyze spatial relationships and patterns. - Each thematic map represents a specific type of data, such as land use, vegetation, or population density. - By overlaying these maps, you can create a new map that combines the information from the individual layers, allowing for more comprehensive analysis. - Thematic overlays can be performed using both vector and raster data Example: Find optimum rice growing areas Optimum conditions to grow rice: - rainfall required for high yield is 10mm/d - Soil type required is sandy loam By combining the rainfall map and the soil type map it is much easier to find the best location. Numeric value can be assigned to the amount of rainfall and each soil type. This makes it easier, on the resulting map, to see where the optimum growing area is located Proximity Analysis - a technique used to determine the spatial relationships between geographic features based on their distance from one another. This type of analysis helps answer questions like \"What\'s near what?\" and \"How far is one feature from another?\" - Proximity tools can be divided into two categories depending on the type of input the tool accepts: features or rasters. The feature-based tools vary in the types of output they produce. - Proximity analysis allows you to Buffer with Multiple Ring buffers selected features that fall with a specified distance range. - The area of intersection between all three buffers will approximate an area of common interest. - Proximity Analysis can be applies and performed in many contexts depending on Intersection is the common area the problem under study. 1. Feature - For feature data, the tools found in the Proximity toolset can be used to discover proximity relationships. These tools output information with buffer features or tables. Buffers are usually used to delineate protected zones around features or to show areas of influence. - Buffer: Creates a zone around a feature at a specified distance. For example, you might create a buffer around a river to identify areas at risk of flooding - Buffer and Multiple Ring Buffer create area features at a specified distance (or several specified distances) around the input features. - The two basic methods for constructing buffers, Euclidean and geodesic - Euclidean buffers measure distance in a two-dimensional Cartesian plane, where straight-line or Euclidean distances are calculated between two points on a flat surface (the Cartesian plane). Euclidean buffers are the more common type of buffer and are appropriate when you\'re analyzing distances around features in a projected coordinate system that are concentrated in a relatively small area (such as one UTM zone). - Geodesic buffers account for the actual shape of the earth (an ellipsoid, or more properly, a geoid). Distances are calculated between two points on a curved surface (the geoid) as opposed to two points on a flat surface (the Cartesian plane). You should always consider creating geodesic in the following circumstances: - Your input features are dispersed (cover multiple UTM zones, large regions, or even the entire globe). - The spatial reference (map projection) of your input features distorts distances to preserve other properties such as area. 2. Raster - The [ArcGIS Spatial Analyst extension](https://desktop.arcgis.com/en/arcmap/latest/extensions/spatial-analyst/what-is-the-spatial-analyst-extension.htm) provides several sets of tools that can be used in proximity analysis. - The [Distance toolset](https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/an-overview-of-the-distance-tools.htm) contains tools that create rasters showing the distance of each cell from a set of features or that allocate each cell to the closest feature. Distance tools can also calculate the shortest path across a surface or the corridor between two locations that minimizes two sets of costs. Distance surfaces are often used as inputs for overlay analyses; for example, in a model of habitat suitability, distance from streams could be an important factor for water-loving species, or distance from roads could be a factor for timid species. - Euclidean distance - a measure of the straight-line distance between two points in Euclidean space. - In GIS, Euclidean Distance analysis is used to calculate the shortest distance from each cell in a raster to the nearest source cell. - Euclidean distance is straight-line distance. For a given set of input features, the minimum distance to a feature is calculated for every cell. - The Euclidean Distance is used to find the nearest point or area of interest Interpolation - a method used to estimate unknown values at specific locations based on known values from surrounding points.  - Interpolation allows you to create a surface from point values of concentration/ magnitude outward over a given area or area of interest - This technique is essential for creating continuous surfaces from discrete data points, such as predicting elevation, rainfall, or temperature across a landscape - Common interpolation techniques: - **Inverse Distance Weighted (IDW)**: Assumes that points closer to each other are more similar than those farther apart. It assigns more weight to nearby points when estimating values - **Kriging**: A geostatistical method that not only considers the distance between points but also the spatial arrangement and statistical properties of the data - **Spline**: Fits a smooth surface through the known data points, minimizing overall surface curvature - **Natural Neighbor**: Uses natural neighbor interpolation to estimate values based on the closest subset of input samples - Spatial interpolation - A process of creating a surface based on values at isolated sample points. - Sample points are locations recorded spatial coordinates where data is collected on some phenomenon - Spatial interpolation uses mathematical estimation to "guess at" what the values are "in between" those points - Output can be created in either a raster or vector interpolated surface - Interpolation is used because field data are expensive to collect, and can't be collected everywhere 3D Models and surface analysis - The physical world exists in 3D whereas maps represent real landscapes as 2D. - The capability of GIS to produce dynamic and attractive three dimensional maps of its most exciting benefits. - Map makers use a range of visual symbols to show height information and create the illusion of an undulating surface: - Contours - Spot height symbols - Hill shading - Cliff and slope symbols - Viewpoint symbols **3D analysis in GIS** involves using three-dimensional data to analyze and visualize spatial relationships and patterns that are not easily represented in two dimensions. This type of analysis is essential for understanding complex terrains, urban environments, and other spatial phenomena. Here are some common types of 3D analysis in GIS: 1. **3D Geometric Analysis**: - **Buffer 3D**: Creates a 3-dimensional buffer around points, lines, or polygons. - **Intersect 3D**: Finds the intersection of 3-dimensional features, maintaining overlapping features. - **Near 3D**: Measures the distance in three dimensions from each input feature to the nearest feature. 2. **3D surface analysis** in GIS involves examining and interpreting the characteristics of three-dimensional surfaces to understand spatial relationships and patterns. This type of analysis is crucial for various applications, such as terrain modeling, urban planning, and environmental studies. Here are some common types of 3D surface analysis: - **Slope Analysis**: Determines the steepness or incline of a surface. This is useful for identifying areas prone to erosion or landslides  - **Aspect Analysis**: Identifies the compass direction that a slope faces, which is important for understanding sunlight exposure and wind patterns A screenshot of a computer generated image Description automatically generated - **Hillshade Analysis**: Creates a shaded relief map by simulating the illumination of a surface from a light source. This helps visualize terrain features A close-up of a map Description automatically generated - **Viewshed Analysis:** Determines the visible areas from a specific point, considering the terrain\'s elevation. This is useful for planning observation points or communication towers  - **Contour Mapping**: Generates contour lines that represent areas of equal elevation, helping to visualize the terrain\'s shape - **Volume Calculation**: Measures the volume of material above or below a reference surface, useful in mining and construction 3. **3D Interpolation**: A comparison of a mountain range Description automatically generated with medium confidence - **Interpolation**: Estimates unknown values by adding a third dimension to the analysis, using methods like kriging, IDW, and spline. 3D analysis tools are widely used in various fields, including urban planning, environmental management, and engineering, to provide more accurate and detailed insights into spatial data Basic data structures for elevation 1. Vector Elevation Data Structure used to represent the elevation of the terrain using vector data models. These structures typically include points, lines, and polygons to depict the elevation and shape of the terrain. Here are the main types of vector elevation data structures: - Spot height/ LIDAR data - These are individual points with known elevation values. Spot heights are often used in conjunction with other vector data structures to provide additional elevation information - Contour lines - These are lines that connect points of equal elevation. Contour lines are commonly used in topographic maps to represent the terrain\'s shape and elevation changes - Triangulated Irregular Network (TIN): a vector-based representation of the terrain that uses a network of non-overlapping triangles. Each triangle\'s vertices are points with known elevation values, and the triangles collectively represent the terrain\'s surface. Constructed by triangulating a set of vertices (points). The vertices are connected with a series of edges to form a network of triangles.  - Here are some key features of TIN surfaces: **Vertices**: Points with known X, Y, and Z coordinates (elevation). **Edges**: Lines connecting the vertices to form triangles. **Triangles**: The basic units of a TIN, each representing a portion of the surface - TIN surfaces are particularly useful for representing complex terrains with varying elevations, such as ridges and valleys.  2. Raster Elevation Data Structure represent the terrain\'s elevation using a grid of cells, where each cell contains an elevation value.  - Digital Elevation Models (DEMs): These are the most common type of raster elevation data. DEMs represent the terrain\'s surface with a grid of regularly spaced cells, each containing an elevation value Developing a DTM ( Digital Terrain Model) Here\'s a general overview of the process: 1. **Data Collection**: Gather elevation data from sources such as LiDAR, photogrammetry, or satellite imagery. LiDAR is often preferred for its high accuracy and detail 2. **Point Cloud Processing**: If using LiDAR, process the raw point cloud data to classify points into categories like ground, vegetation, and buildings. This step is crucial for isolating ground points needed for the DTM 3. **Filtering and Cleaning**: Remove non-ground points (e.g., vegetation, buildings) to ensure the DTM represents the bare-earth surface 4. **Interpolation**: Use interpolation methods to create a continuous surface from the ground points. Common methods include Triangulated Irregular Network (TIN) and raster grid interpolation 5. **DEM Generation**: Convert the interpolated surface into a raster grid, where each cell contains an elevation value. This raster grid forms the basis of the DTM 6. **Quality Control**: Check the DTM for accuracy and completeness. Correct any errors or gaps to ensure a high-quality elevation model 7. **Export and Use**: Export the DTM in a suitable format for your GIS software. DTMs can be used for various applications, such as hydrological modeling, flood simulation, and infrastructure planning