1. 1 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Analysis and Modeling in GIS

2. 2 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals GIS and the Levels of Science Description: Using GIS to create descriptive models of the world --representations of reality as it exists. Analysis: Using GIS to answer a question or test an hypothesis. Often involves creating a new conceptual output layer, (or table or chart), the values of which are some transformation of the values in the descriptive input layer. --e.g. buffer or slope or aspect layers Prediction: Using GIS capabilities to create a predictive model of a real world process, that is, a model capable of reproducing processes and/or making predictions or projections as to how the world might appear. --e.g. flood models, fire spread models, urban growth models

3. 3 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals The Analysis Challenge Recognizing which generic GIS analytic capability (or combination) can be used to solve your problem: –meet an operational need –answer a question posed by your boss or your board –address a scientific issue and/or test a hypothesis »Determine the acreage of agricultural, residential, commercial and industrial land which will be lost by construction of new highway corridor. »Do households affected by cholera are near to water source? »Are gas stations or fast food joints closer to freeways? »Are schools experiencing drop out closer to fish markets?

4. 4 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Description and Basic Analysis (Table of Contents) Spatial Operations Vector –spatial measurement –Centrographic statistics –buffer analysis –spatial aggregation »redistricting »regionalization »classification –Spatial overlays and joins Raster –neighborhood analysis/spatial filtering –Raster modeling Attribute Operations –record selection »tabular via SQL »‘information clicking’ with cursor –variable recoding –record aggregation –general statistical analysis –table relates and joins

5. 5 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Spatial measurements: distance measures –between points –from point or raster to polygon or zone boundary –between polygon centroids polygon area polygon perimeter polygon shape volume calculation –e.g. for earth moving, reservoirs direction determination –e.g. for smoke plumes Spatial operations: Spatial Measurement ArcGIS geodatabases contain automatic variables: shape.length: line length or polygon perimeter shape.area: polygon area Automatically updated after editing. For shapefiles, these must be calculated e.g. by opening attribute table and applying Calculate Geometry to a column (AV 9.2)

6. 6 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Spatial operations: Spatial Measurement Area and Perimeter measures are automatically maintained in the attributes table for a Geodatabase or coverage. For a shapefile, you need to apply Calculate Geometry to an appropriate column in the attribute table (or convert to a geodatabase). The shape index can be calculated from the area and perimeter measurements. (Note: shapefile and shape index are unrelated)

7. 7 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Spatial Measurement: Calculating the Area of a Polygon 010 5 0 5 0 0 5 0 5 2,3 7,7 7,3 6,2 4,7 Area=(2 x 4)/2=4 Area=(3 x 4)=12 Area=(5 x 1)/2=2.5 5 010 5 5 0 5 = - A CB = - The actual algorithm used obtains the area of A by calculating the areas of B and C, and then subtracting. The actual formulae used is as follows: Its implementation in Excel is shown below. The area of the above polygon is 18.5, based on dividing it into rectangles and triangles. However, this is not practical for a complex polygon. Area of triangle = (base x height)/2

8. 8 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Spatial Operations: Centrographic Statistics Basic descriptors for spatial point distributions Two dimensional (spatial) equivalents of standard descriptive statistics (mean, standard deviation) for a single-variable distribution Measures of Centrality (equivalent to mean) –Mean Center and Centroid Measures of Dispersion (equivalent to standard deviation or variance) –Standard Distance –Standard Deviational Ellipse Can be applied to polygons by first obtaining the centroid of each polygon Best used in a comparative context to compare one distribution (say in 1990, or for males) with another (say in 2000, or for females)

9. Spatial Operations: buffer zones region within ‘x’ distance units buffer any object: point, line or polygon use multiple buffers at progressively greater distances to show gradation may define a ‘friction’ or ‘cost’ layer so that spread is not linear with distance Implement in Arcview 3.2 with Theme/Create buffers in ArcGIS 8 with ArcToolbox>Analysis Tools>Buffer Examples 200 foot buffer around property where zoning change requested 100 ft buffer from stream center line limiting development 3 mile zone beyond city boundary showing ETJ (extra territorial jurisdiction) use to define (or exclude) areas as options (e.g for retail site) or for further analysis in conjunction with ‘friction layer’, simulate spread of fire polygon buffer line buffer point buffers Note: only one layer is involved, but the buffer can be output as a new layer

10. Criteria may be: –formal (based on in situ characteristics) e.g. city neighborhoods –functional (based on flows or links): e.g. commuting zones Groupings may be: –contiguous –non-contiguous Boundaries for original polygons: –may be preserved –may be removed (called dissolving) Examples: elementary school zones to high school attendance zones ( functional districting ) election precincts (or city blocks) into legislative districts ( formal districting ) creating police precincts ( funct. reg.) creating city neighborhood map ( form. reg. ) grouping census tracts into market segments--yuppies, nerds, etc ( class. ) creating soils or zoning map ( class ) Implement in ArcView 9 thru ArcToolbox>Generalization>Dissolve Spatial Operations: spatial aggregation districting/redistricting –grouping contiguous polygons into districts –original polygons preserved Regionalization (or dissolving) –grouping polygons into contiguous regions –original polygon boundaries dissolved classification –grouping polygons into non- contiguous regions –original boundaries usually dissolved –usually ‘formal’ groupings Grouping/combining polygons—is applied to one polygon layer only.

11. 11 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Districting: elementary school attendance zones grouped to form junior high zones. Regionalization: census tracts grouped into neighborhoods Classification: cities categorized as central city or suburbs soils classified as igneous, sedimentary, metamorphic

12. 12 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Spatial Operations: Spatial Matching: Spatial Joins and Overlays combine two (or more) layers to: – select features in one layer, &/or – create a new layer used to integrate data having different spatial properties (point v. polygon), or different boundaries (e.g. zip codes and census tracts) can overlay polygons on: –points (point in polygon) –lines (line on polygon) –other polygons (polygon on polygon) –many different Boolean logic combinations possible »Union (A or B) »Intersection (A and B) » A and not B ; not (A and B) can overlay points on: –Points, which finds & calculates distance to nearest point in other theme –Lines, which calculates distance to nearest line Examples assign environmental samples (points) to census tracts to estimate exposure per capita (point in polygon) identify tracts traversed by freeway for study of neighborhood blight (polygon on lines) integrate census data by block with sales data by zip code (polygon on polygon) Clip US roads coverage to just cover Texas (polygon on line) Join capital city layer to all city layer to calculate distance to nearest state capital (point on point)

13. 13 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals ERASE - erases the input coverage features that overlap with the erase coverage polygons. CLIP - extracts those features from an input coverage that overlap with a clip coverage. This is the most frequently used polygon overlay command to extract a portion of a coverage to create a new coverage. Example: Spatial Matching: Clipping and Erasing (sometimes referred to as spatial extraction)

14. Note: the definition of Union in GIS is a little different from that in mathematical set theory. In set theory, the union contains everything that belongs to any input set, but original set membership is lost. In a GIS union, all original set memberships are explicitly retained. In set theory terms, the outcome of the above would simply be: Example: Spatial Matching via Polygon-on-Polygon Overlay: Union Drainage Basins The two layers (land use & drainage basins) do not have common boundaries. GIS creates combined layer with all possible combinations, permitting calculation of land use by drainage basin. a. b. c. aG aA bA bG cA cG Land Use A. G. Atlantic Gulf Combined layer GIS Union Set Theory Union 1 2 3 Another example

15. 15 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Attribute Operations: record selection or extraction --features selected on the map are identified in the table (and visa versa) Select by Attribute (tabular) Independent selection by clicking table rows: –Open Attribute Table & click on grey selection box at start of row (hold ctrl for multiple rows) Create SQL query – use Selection/Select by Attribute use table Relates /Joins to select specific data Select by Graphic Manually, one point at a time –use Select Features tool within a rectangle or an irregular polygon –use Selection/Select by Graphic within a radius (circle) around a point or points –use Selection/Select by Location (are wthin distance) Select by Location By using another layer –Use Selection/Select by Location (same as Spatial Matching discussed previously) Hot Link Click on map to ‘hot link’ to pictures, graphs, or other maps Outputs may be: Simultaneously highlighted records in table, and features on map New tables and/or map layers Examples Use SQL query to select all zip codes with median incomes above $50,000 (tabular) identify zip codes within 5 mile radius of several potential store sites and sum household income (graphic) show houses for sale on map, and click to obtain picture and additional data on a selected house (hot link)

16. 16 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Attribute Operations: statistical analysis on one or more columns in table univariate (one variable or column) –central tendency: mean, median, mode –dispersion: standard deviation, min, max –To obtain these statistics in ArcGIS: »Right click in T of C and select Open attribute table »Right click on column heading and select Statistics bivariate (relating two variables or columns) –interval and nominal scale variables: sum or mean by category » average crop yield by silt-sand-clay soil types »To implement in ArcGIS, proceed as above but use Summarize –two interval scale variables: correlation coefficients »income by education »ArcScripts are available for this on ESRI web site (or use Excel!) multivariate (more than two variables) –usually requires external statistical package such as SAS, SPSS, STATA or S- PLUS

17. 17 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Advanced Applications: Proximity Analysis Nearest Neighbor location (distance) relative to nearest neighbor ( points or polygon centroids) location (distance) relative to nearest objects of selected other types (e.g. to line, or point in another layer, or polygon boundary) Requires only one output column –altho generalizable to k th nearest neighbor Full matrix measure location of each object relative to every other object –requires output matrix with as many columns as rows in input table Point Pattern Analysis is pattern? Random Clustered Dispersed Requires the application of Spatial Statistics such as Nearest neighbor statistic Moran’s I which are based on proximity of points to each other ArcToolbox>Spatial Statistics Tools

18. 18 9/30/2016 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals Advanced Applications: Network Analysis Routing shortest path between two points – direction instructions (locating hotel from airport) travelling salesman: shortest path connecting n points –bus routing, delivery drivers Network-based Districting expand from site along network until criteria (time, distance, cost, object count) is reached; then assign area to district –creating market areas, attendance zones, etc –essentially network-based buffering Network-based Allocation assign locations to the nearest center based upon travel thru network –assign customers to pizza delivery outlets draw boundaries (lines of equidistance between 2 centers) based on the above –Network-based market area delimitation –Essentially, network-based polygon tesselation In all cases, ‘distance’ may be measured in miles, time, cost or other ‘friction’ (e.g pipe diameter for water, sewage, etc.). Arc or node attributes (e.g one-way streets, no left turn) may also be critical.