SPATIAL DATA ANALYSIS

Spatial analysis Spatial analysis is the vital part of GIS. Spatial analysis in GIS involves three types of operations attribute query (also known as non-spatial), spatial query and generation of new data sets from the original databases.

SPATIAL DATA ANALYSIS Representation of reality Purpose is to understand, describe, predict the real world scenarios Gives a simplified , manageable view of the real world

Spatial Search/Query Overlay is a spatial retrieval operation that is equivalent to an attribute join. Buffering is a spatial retrieval around points, lines, or areas based on distance.

Find all houses within a certain area that have tiled roofs and five bedrooms, then list their characteristics.

Buffering can be constructed around a point, line or area. Buffering algorithm creates a new area enclosing the buffered object. The applications of this buffering operations include, for example, identifying protected zone around lakes and streams, zone of noise pollution around highways, service zone around bus route, or groundwater pollution zone around waste site.

Spatial Overlay An operation that merges the features of two coverage layers into a new layer and relationally joins their feature attribute table. When overlay occurs, spatial relationships between objects are updated for the new, combined map. In some circumstances, the result may be information about relationships (new attributes) for the old maps rather than the creation of new objects.

GIS usage in Spatial Analysis GIS operational procedure and analytical task that are particularly useful for spatial analysis Single layer operations Multi layer operations/ Topological overlay Spatial modeling Geometric modeling Calculating the distance between geographic features Calculating area, length and perimeter Geometric buffers. Point pattern analysis Network analysis Surface analysis Raster/Grid analysis Fuzzy Spatial Analysis Geostatistical Tools for Spatial Analysis While basic spatial analysis involves some attribute queries and spatial queries, complicated analysis typically require a series of GIS operations including multiple attribute and spatial queries, alteration of original data, and generation of new data sets. The methods for structuring and organizing such operations are a major concern in spatial analysis. An effective spatial analysis is one in which the best available methods are appropriately employed for different types of attribute queries, spatial queries, and data alteration. The design of the analysis depends on the purpose of study.

Point pattern analysis It deals with the examination and evaluation of spatial patterns and the processes of point features. Distribution of an endangered species examined in a point pattern analysis .

Vector Based Spatial Data Analysis There are multi layer operations, which allow combining features from different layers to form a new map and give new information and features that were not present in the individual maps. Topological overlays: Selective overlay of polygons, lines and points enables the users to generate a map containing features and attributes of interest, extracted from different themes or layers.

1. Point-in-polygon overlay Map overlay - point in polygon Topological overlays 1. Point-in-polygon overlay Point-in-polygon algorithm overlays point objects on areas and compute "is contained in" relationship. The result is a new attribute for each point specifying the polygon it belongs to. Map overlay - point in polygon

Topological overlays (cont.) 2. Line-on-polygon overlay Line-on-polygon algorithm overlays line objects on area objects and compute "is contained in" relationship. Lines are broken at each area object boundary to form new line segments and new attributes created for each output line specifying the area it belongs to. Output is line coverage with additional attribute. No polygon boundaries are copied. New arc-node topology is created.

Topological overlays (cont.) 3. Polygon-on-polygon overlay Polygon-on-polygon algorithm overlay two layers of area objects. Boundaries of polygons are broken at each intersection and new areas are created. During polygon overlay, many new and smaller polygons may be created, some of which may not represent true spatial variations. Polygon-in-polygon overlay: Output is polygon coverage. Coverages are overlaid two at a time. There is no limit on the number of coverages to be combined. New File Attribute Table is created having information about each newly created feature.

NETWORK ANALYSIS: Designed specifically for line features organized in connected networks, typically applies to transportation problems and location analysis such as school bus routing, passenger plotting, walking distance, bus stop optimization, optimum path finding etc.

SURFACE ANALYSIS Deals with the spatial distribution of surface information in terms of a three-dimensional structure. The distribution of any spatial phenomenon can be displayed in a three dimensional perspective diagram for visual examination. Surface analysis deals with the spatial distribution of surface information in terms of a three-dimensional structure. The distribution of any spatial phenomenon can be displayed in a three dimensional perspective diagram for visual examination. A surface may represent the distribution of a variety of phenomena, such as population, crime, market potential, and topography, among many others. The perspective diagram in represents topography of the terrain, generated from digital elevation model (DEM) through a series of GIS-based operations in surface analysis.

GRID ANALYSIS Involves the processing of spatial data in a special, regularly spaced form. The following illustration shows a grid-based model of fire progression. The darkest cells in the grid represent the area where a fire is currently underway. A fire probability model, which incorporates fire behavior in response to environmental conditions such as wind and topography, delineates areas that are most likely to burn in the next two stages. Lighter shaded cells represent these areas. Fire probability models are especially useful to fire fighting agencies for developing quick-response, effective suppression strategies.

INTERPOLATION Method to estimate variables based on values at observed locations. Assumption The influence of one known point over an unknown point increases as distance between them decreases. 4 methods included:

a) Inverse distance weighting - reduce the variable with decreasing nearness from observed location b) Kriging method -interpolates space according to spatial lag relationship with both systematic & random components c)Thiessen mehod d)Spline method

ACCURACY OF INTERPOLATION Depends on accuracy, number and distribution of the known points used in the calculation Depends on how accurate the mathematical function used correctly models the phenomenon. As the assumptions of the model are more severely violated, the interpolation results become less accurate. No matter which interpolator is selected, the more input points and the greater their distribution, the more reliable the results.

Raster Based Spatial Data Analysis A raster is a GIS data structure comprised of a matrix of rectangular grid cells. Each cell represents a specific area on the ground. Resolution of raster is defined by the ground area represented by the raster grid cell. The higher the resolution of the grid, the more cells are required to portray a given area of ground surface. All cells in a grid have a positive position reference, following the left-to-right and top-to-bottom data scan. Every cell in a grid is an individual unit and must be assigned a value. Depending on the nature of the grid, the value assigned to a cell can be an integer or a floating point. When data values are not available for particular cells, they are described as NODATA cells. NODATA cells differ from cells containing zero in the sense that zero value is considered to be data.

The resolution of raster is often a function of the scale of the map from which the spatial data may have been scanned or digitized. In raster analysis, geographic units are regularly spaced and the location of each unit is referenced by row & column positions. Because geographic units are of equal size & identical shape, area adjustment of geographic units is unnecessary & spatial properties of geographic entities are relatively easy to trace

ADVANTAGES OF USING THE RASTER FORMAT IN SPATIAL ANALYSIS Efficient processing: Because geographic units are regularly spaced with identical spatial properties, multiple layer operations can be processed very efficiently. Numerous existing sources: Grids are the common format for numerous sources of spatial information including satellite imagery, scanned aerial photos, and digital elevation models, among others. Different feature types organized in the same layer: For instance, the same grid may consist of point features, line features, and area features, as long as different features are assigned different values Efficient processing: Because geographic units are regularly spaced with identical spatial properties, multiple layer operations can be processed very efficiently. Numerous existing sources: Grids are the common format for numerous sources of spatial information including satellite imagery, scanned aerial photos, and digital elevation models, among others. These data sources have been adopted in many GIS projects and have become the most common sources of major geographic databases. Different feature types organized in the same layer: For instance, the same grid may consist of point features, line features, and area features, as long as different features are assigned different values

Raster Overlay A Replace all 0’s in B with data from A B

PIXELS A term employed in the field of remote sensing. Like grid cell, portray an area subdivided into very small square cells. The result of capturing data through the digitization of aerial/satellite imagery. Image resolution is stated by defining the ground area represented by one pixel. Identified by unique numerical codes called a digital number. Each cell has only one digital number.

Grid Format Disadvantages Data redundancy: When data elements are organized in a regularly spaced system, there is a data point at the location of every grid cell, regardless of whether the data element is needed or not. Resolution confusion: Gridded data give an unnatural look and unrealistic presentation unless the resolution is sufficiently high. Conversely , spatial resolution dictates spatial properties. For instance, some spatial statistics derived from a distribution may be different, if spatial resolution varies, which is the result of the well-known scale problem. Cell value assignment difficulties: Different methods of cell value assignment may result in quite different spatial patterns. Data redundancy: When data elements are organized in a regularly spaced system, there is a data point at the location of every grid cell, regardless of whether the data element is needed or not. Although, several compression techniques are available, the advantages of gridded data are lost whenever the gridded data format is altered through compression. In most cases, the compressed data cannot be directly processed for analysis. Instead, the compressed raster data must first be decompressed in order to take advantage of spatial regularity. 􀁺 Resolution confusion: Gridded data give an unnatural look and unrealistic presentation unless the resolution is sufficiently high. Conversely, spatial resolution dictates spatial properties. For instance, some spatial statistics derived from a distribution may be different, if spatial resolution varies, which is the result of the well-known scale problem. 􀁺 Cell value assignment difficulties: Different methods of cell value assignment may result in quite different spatial patterns.

Reclassification Reclassification is to reassign new thematic values or codes to units of spatial feature, which will result in merging polygons. A set of "reclassify attributes", "dissolve the boundaries" and "merge the polygons" are used frequently in aggregating area objects

-Altering attribute values without changing geometry. Classification A-B : agriculture soil C-E : non agriculture soil Soil map Agricultural soil map -Altering attribute values without changing geometry. -to see new pattern and connection

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