GEOGRAPHIC INFORMATION SYSTEMS Lecture 18: Spatial Modeling

Spatial Analysis in GIS (cont d) GEOGRAPHIC INFORMATION SYSTEMS Lecture 18: Spatial Modeling - the basic types of analysis that can be accomplished with a GIS are outlined in The Esri Guide to GIS Analysis - in class I briefly described the 11 types of analysis - the notes below expand on each of the different types of analysis and provide some real world examples 1) Basic measurement - can use the basic Measure tool or the Point Distance tool in the ArcToobox a) distance: - distance between point features - distance between line features - distance between polygon features - or any combination of the above b) length - distance along a line feature - calculate the distance along a road, pipeline or stream segments - perimeter of a polygon - calculate the length of fenced needed to enclose a pasture b) area - area of a polygon or multiple polygons - calculate the total area of a land use types in a city, county or state note: if you import a shapefile to a geodatabase, ArcGIS automatically calculates a length and area field - for a line layer, the length-field value is the length of each line feature - for a polygon layer, the area-field value is the area and the length-field value is the perimeter - the values calculated in these length and area fields are based upon the units of the projection - if the layer is unprojected, the length and area values are decimal degrees (which is meaningless) - if the layer is projected, the length values will be in meters or ft and area values in m 2 or ft 2 2) Mapping where things are - mapping individual features (unique events), feature types, feature subsets, and feature categories a) mapping individual features (or unique events) - mapping a single point, line, or polygon - location of a single crime (point) - location of a parade route (line) - single parcel of land selected for development (polygon) b) mapping individual feature types - mapping all points, lines, or polygons in a layer - location of all crimes (points) - location of all roads (lines) - location of all land parcels (polygons) c) map a subset of feature types - use a definition query to map a subset of all features in a layer - map burglaries (a subset of all crimes) - map major highways (a subset of roads) - map commercial land parcels (a subset of land parcels) d) map by category - data are classified by a qualitative attribute category - map all crimes showing different types (assaults, burglaries, robberies) - map all streets showing different types (primary roads, secondary roads, highways) - map all parcels showing different types (commercial, industrial, residential) e) recognizing patterns from the distribution of features - note that you can often infer something about the process based on spatial patterns - distribution of rainfall with respect to terrain (recognize orographic effects) - distribution automobile accidents (recognize dangerous intersections) Copyright Kevin Mulligan, Texas Tech University

3) Mapping the most and the least - involves the classification of data based upon quantities - the quantities can be either counts, amounts, ratios, or ranks - classified using natural breaks, quantile, equal interval (defined interval), or standard deviation - normally use graduated colors or graduated symbols to show feature classes a) counts - map the total number associated with features - counties mapped by total population (graduated color) - businesses mapped by number of employees (graduated symbol) b) amounts - map the total value or magnitude associated with each feature - gross tax revenue by county (graduated color) - per student school expenditure (graduated symbol) - earthquake magnitudes (graduated symbol) c) ratios - map the relationship between two variables - evens out differences between large and small areas - most common ratios are averages, proportions, and densities - averages - divide quantities that use different measures - people per household = population / number of households - proportions (same as percentages) - divide quantities that use the same measure - percent elderly = population over 65 / population - densities - divide a quantity by the area of the feature - population per square mile = population / square miles d) ranks - features are ranked from low to high - shows relative values (poor, fair, good, excellent - or - low, medium, high) - used when it is difficult to quantify an attribute - relative value of scenic streams - soil types used for growing a particular crop - used when a range of values is adequate for a particular purpose - permeability of soils for a septic system 4) Mapping density - shows where the highest density (or concentration) of a feature is located - the results of a density analysis can be mapped using a continuous shaded surface and/or isolines - the density analysis can be weighted by a feature attribute - e.g. measured value of a chemical in point water samples (points) - e.g. traffic counts along different street segments (lines) - e.g. playa lakes weighted by area of each lake (polygon centroids) a) can be mapped as a point density map - map of crimes in a city (point density analysis) b) can be mapped as a line density map - map of street network in a city (line density analysis) c) polygon data must be converted to polygon centroids, then apply a point density analysis 5) Overlay Analysis (finding what's inside) - overlay polygons to select points, lines or polygons a) points in polygons - wells within a county b) lines in polygons - streams in a county c) polygons within polygons - soils in a county Copyright Kevin Mulligan, Texas Tech University

6) Overlay Analysis (finding what intersects) - overlay lines on lines or lines on polygons to find features that intersect a) lines on lines - roads that intersect streams b) lines on a polygon - roads that intersect a flood zone 7) Proximity Analysis (finding what's nearby) - proximity can be measured in terms of straight line or (Euclidian distance) or distance/cost along a network a) straight line distance - distance between features (same as basic measurement) - buffers around points, lines, or polygons (often used to select other features) - distance surface (raster grid where cell values represent the distance from a feature or features) b) distance or cost over a network - miles over a road network or travel time over a road network 8) Adjacency (finding what s adjacent) - same as a proximity analysis with the distance set to zero a) select polygons adjacent to other polygons (same feature type) - identify parcels adjacent to a parcel with a proposed zoning change 9) Change Analysis (mapping change over time) - mapping the change in location, character or magnitude over time - can use animations to represent the time series a) change in location (tracking map) - animal migration, vehicle tracking, spread of a forest fire b) change in character (time series of maps) - land use change over time, deforestation, urban expansion c) change in magnitude (difference map) - population change over time: county population 21 minus county population 2 1) Visualizing output from numerical models a) mapping the output from numerical models (and subsequent spatial analysis) - e.g. using the output from a hydrologic model (water surface elevation) to map flood zones - e.g. using the output from an atmospheric dispersion model to map the movement of a plume 11) Spatial modeling (or suitability analysis) - in this case, GIS used as a decision support tool - uses map algebra to combine variables (layers) in the model a) where is the best location to? - e.g. locate a new pipeline, school or retail store b) where is the most likely occurrence of...? - e.g. the potential for a forest fire or disease outbreak Spatial Modeling and Map Algebra - spatial models are often developed as a decision support tool Where is the best location to? Where is the most likely occurrence of? - to address these types of questions, we can develop a spatial model and run a site suitability analysis - spatial models involve the use of map algebra to integrate and analyze the data contained in two or more layers - the three basic types of spatial models are: 1) a Boolean model, 2) a weighted model and 3) a combined model 1) Boolean model - in a Boolean model variables (layers) are analyzed to determine whether a location is suitable or not - the Boolean model can be developed using either vector polygon layers or discrete raster layers - in either case, the features in each layer are reclassified and assigned a value of 1 or = not acceptable (or not suitable) 1 = acceptable (or suitable) Copyright Kevin Mulligan, Texas Tech University

- once the features in each layer are reclassified ( or 1), the layers are multiplied to determine suitability - in a raster analysis, this would be the grid cells in layer one x grid cells in layer two, etc - in a vector analysis, this would be the features in layer one x features in layer two, etc. - when the layers are multiplied there are four possible outcomes x = not suitable x 1 = not suitable 1 x = not suitable 1 x 1 = 1 suitable areas - only those grid cells or features that have a value of 1 are suitable - also note that a Boolean analysis is not limited to two layers - an analysis can include many different variables (layers), e.g. x x 1 x x 1 = not suitable - the diagram below illustrates the geoprocessing that might be required to create a Boolean model - in this example we are trying to find good soils (classified as Cp) that are not subject to flooding Marmot Habitat Suitability Model To illustrate how a Boolean analysis works, we developed a model to find the most suitable habitat for marmots located on public land. The suitability model was based upon: - a landcover layer (barren/rock, grassland/herbaceous) - a land ownership layer (selecting for state and federal land), - a soils layer (selecting for rocky soils and rock outcrops), - and an elevation layer (selecting elevations above 75 feet). To develop the model, we reclassified the grid cells in each of the four layers into s and 1s (a Boolean reclass). We then used the Raster Calculator to multiply the grid cells in the four layers and create a final suitability layer. To run the analysis more efficiently, we could also develop the model using Model Builder and save the model in ArcToolbox. When we use Model Builder and save the model, we can run the same analysis in different areas and we can share the model with others. Copyright Kevin Mulligan, Texas Tech University

GIST 33 / 53 Spatial Modeling Spatial Analysis in GIS (cont d) Esri Guide to GIS Analysis Spatial Modeling (Site Suitability Analysis) Boolean model Weighted Model Combined Model

Spatial Analysis in GIS Volume 2: Spatial Measurements and Statistics Volume 1: Analyzing Geographic Patterns and Relationships

Spatial Analysis in GIS 1) Basic measurement - distance, length and area 2) Mapping where things are - features, feature types, feature subsets, and feature categories 3) Mapping the most and the least - classification of quantitative data: choropleth maps 4) Density Analysis (create a density surface) - density of features (points or lines) - density of concentration based on feature attributes 5) Overlay Analysis (finding what's inside) - overlay polygons to select points, lines and polygons within polygons 6) Overlay Analysis (finding what intersects) - overlay lines on lines or lines on a polygon

Spatial Analysis in GIS 7) Proximity Analysis (finding what's nearby) - straight line (Euclidian) distance or distance along a network 8) Adjacency (finding what s adjacent) - proximity analysis with distance set to zero - select polygons adjacent to other polygons (same feature type) 9) Change Analysis (mapping change over time - can use animations) - change in location (tracking map) - change in character (time series of maps) - change in magnitude (difference map) 1) Visualizing output from numerical models - mapping the output from numerical models 11) Spatial modeling (or site suitability analysis) - GIS is used as a decision support tool - analysis involves the use of map algebra - where is the best location to? - where is the most likely occurrence of...?

Spatial Modeling Map Algebra - map algebra allows us to add or multiply layers of spatial information - can be used to develop three different types of spatial models 1) Boolean analysis 2) Weighted analysis 3) Combined analysis

Spatial Modeling 1) Boolean Model - features in each layer are assigned values of or 1 = not acceptable 1 = acceptable - layers are multiplied Suitability = (Boolean layer 1) x (Boolean layer 2) x (Boolean layer 3) possible feature or grid cell values: x x 1 = x 1 x = 1 x x = 1 x 1 x = x 1 x 1 = 1 x x 1 = 1 x 1 x 1 = 1 (only acceptable features)

Spatial Modeling 1) Boolean Model = not acceptable 1 = acceptable remote sensing image classify land use ag land use ag Boolean Reclass land use 1 1 1 raster image Ra Gp Cp Mc soils layer vector to raster conversion raster polygons (classified discrete raster) Ra Cp Mc Gp soils layer Boolean Reclass raster polygons 1 soils layer OVERLAY and MULTIPLY vector polygons raster polygons (classified discrete raster) raster polygons

Spatial Modeling 1) Boolean Model Example - features in each layer are assigned values of or 1 not acceptable 1 acceptable - layers are multiplied Variables (layers) Elevation Soils Land Ownership Land Cover Marmot

Spatial Modeling with Model Builder Model Builder Interface Marmot Habitat Model