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**Analysis and Modeling in GIS**

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**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

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**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 Send mailings to property owners potentially affected by a proposed change in zoning Determine if a crime occurred within a school’s “drug free zone” Determine the acreage of agricultural, residential, commercial and industrial land which will be lost by construction of new highway corridor Determine the proportion of a region covered by igneous extrusions Do Magnitude 4 or greater sub-oceanic earthquakes occur closer to the Pacific coast of South America than of North America? Are gas stations or fast food joints closer to freeways?

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**Availability of Capabilities in GIS Software**

Descriptive Focus: Basic Desktop GIS packages Data editing, description and basic analysis ArcView Mapinfo Geomedia Analytic Focus: Advanced Professional GIS systems More sophisticated data editing plus more advanced analysis ARC/INFO, MapInfo Pro, etc. Provided through extra cost Extensions or professional versions of desktop packages Prediction: Specialized modeling and simulation via scripting/programming within GIS VB and ArcObjects in ArcGIS Avenue scripts in ArcView 3.2 AMLs in Workstation ARC/INFO (v. 7) Write your own or download from ESRI Web site via specialized packages and/or GISs 3-D Scientific Visualization packages transportation planning packages e.g TransCAD ERDAS, ER Mapper or similar package for raster Capabilities move ‘down the chain’ over time. In earlier generation GIS systems, use of advanced applications often required learning another package with a different user interface and operating system (usually UNIX).

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**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

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**Spatial operations: Spatial Measurement**

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 Comments: Cartesian distance via Pythagorus Used for projected data by ArcMap measure tools Spherical distance via spherical coordinates Cos d = (sin a sin b) + (cos a cos b cos P) where: d = arc distance a = Latitude of A b = Latitude of B P = degrees of long. A to B Used for unprojected data by ArcMap measure tools possible distance metrics: straight line/airline city block/manhattan metric distance thru network time/friction thru network shape often measured by: Projection affects values!!! 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) perimeter area x 3.54 = 1.0 for circle = 1.13 for square Large for complex shape Distances depend on projection. Perimeter to area ratio differs

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**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)

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**Spatial Measurement: Calculating the Area of a Polygon**

10 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 A - B C = 10 5 5 10 5 10 - = 10 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: 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 Its implementation in Excel is shown below.

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**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)

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**Centroid and Mean Center**

balancing point for a spatial distribution analogous to the mean single point representation for a polygon (centroid) single point summary for a point distribution (mean center) can be weighted by ‘magnitude’ at each point (analogous to weighted mean) minimizes squared distances to other points, thus ‘distant’ points have bigger influence than close points ( Oregon births more impact than Kansas births!) is not the point of “minimum aggregate travel”--this would minimize distances (not their square) and can only be identified by approximation. useful for summarizing change over time in a distribution (e.g US pop. centroid every 10 years) placing labels for polygons for weird-shaped polygons, centroid may not lie within polygon centroid outside polygon Note: many ArcView applications calculate only a “psuedo” centroid: the coordinates of the bounding box (the extent) of the polygon Can be implemented via: ArcToolbox>Spatial Statistics Tools>Measuring Geographic Distributions>Mean Center

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**Calculating the centroid of a polygon or the mean center of a set of points.**

10 5 2,3 7,7 7,3 6,2 4,7 (same example data as for area of polygon) 10 5 2,3 7,7 7,3 6,2 4,7 Calculating the weighted mean center. Note how it is pulled toward the high weight point.

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Median Center: Intersection of a north/south and an east/west line drawn so half of population lives above and half below the e/w line, and half lives to the left and half to the right of the n/s line. Same as “point of minimum aggregate travel” the location that would minimize travel distance if we brought all US residents straight to one location. Mean Center: Balancing point of a weightless map, if equal weights placed on it at the residence of every person on census day. Note: minimizes squared distances. The point is considerable west of the median center because of the impact of “squared distance” to “distant” populations on west coast For a fascinating discussion of the effect of population projection see: E. Aboufadel & D. Austin, A new method for calculating the mean center of population center of the US Professional Geographer, February 2006, pp Source: US Statistical Abstract 2003

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**Standard Distance Deviation single unit measure of the spread or dispersion of a distribution.**

Is the spatial equivalent of standard deviation for a single variable Equivalent to the standard deviation of the distance of each point from the mean center Given by: which by Pythagoras reduces to: ---the square root of the average squared distance ---essentially the average distance of points from the center We can also weight each point and calculate weighted standard distance (analogous to weighted mean center.)

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**Standard Distance Deviation Example**

10 5 2,3 7,7 7,3 6,2 4,7 Circle with radii=SDD=2.9

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**Standard Deviational Ellipse: concept**

Standard distance deviation is a good single measure of the dispersion of the incidents around the mean center, but it does not capture any directional bias doesn’t capture the shape of the distribution. The standard deviation ellipse gives dispersion in two dimensions Defined by 3 parameters Angle of rotation Dispersion along major axis Dispersion along minor axis The major axis defines the direction of maximum spread of the distribution The minor axis is perpendicular to it and defines the minimum spread

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**Standard Deviational Ellipse: example**

There appears to be no major difference between the location of the software and telecommunications industry in North Texas. For formulae for its calculation, see Lee and Wong Statistical Analysis with ArcView GIS pp (1st ed.), pp (2nd ed.)

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**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 polygon 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 point buffers line buffer Note: only one layer is involved, but the buffer can be output as a new layer

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**Spatial Operations: spatial aggregation**

Grouping/combining polygons—is applied to one polygon layer only. 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 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

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**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

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**Spatial Operations: Spatial Matching: Spatial Joins and Overlays**

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) 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

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**Example: Spatial Matching: Clipping and Erasing**

(sometimes referred to as spatial extraction) 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. ERASE - erases the input coverage features that overlap with the erase coverage polygons.

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**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 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: Another example GIS Union 1 Set Theory Union 2 3

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**Implementing Spatial Matching in ArcGIS 9**

Available in three places via Selection/Select by Location this selects features of one layer(s) which relate in some specified spatial manner to the features in another layer if desired, selected features may be saved later to a new theme via Data/Export Data Individual features are not themselves modified via Spatial Join (right click layer in T of C, select Join/Joins and Relates, then click down arrow in first line of Join Data window---see Joining Data in Help for details) Use for: points in polygon lines in polygon points on lines (to calculate distance to nearest line) points on points (to calculate distance to “nearest neighbor” point) operate on tables and normally creates a new table with additional variables, but again does not modify spatial features themselves via ArcToolbox Generally these tools modify geographic feature, thus they create a new layer (e.g. shape file) Tools are organized into multiple categories ArcToolbox Examples Dissolve features based on an attribute Combine contiguous polygons and remove common border ArcToolbox>Generalization>Dissolve Clip one layer based on another ArcToolbox>Analysis Tools>Extract>Clip Use one theme to limit features in another theme (e.g. limit a Texas road theme to Dallas county only) Intersect two layers (extent limited to common area) ArcToolbox>Analysis Tools>Overlay>Intersect Use for polygon on polygon overlay Union two layers (covers full extent of both layers)

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**Spatial Operations: neighborhood analysis/spatial filtering**

spatial convolution or filter applied to one raster layer value of each cell replaced by some function of the values of itself and the cells (or polygons) surrounding it can use ‘neighborhood’ or ‘window’ of any size 3x3 cells (8-connected) 5x5, 7x7, etc. differentially weight the cells to produce different effects kernel for 3x3 mean filter: 1/9 1/9 1/9 low frequency ( low pass) filter: mean filter cell replaced by the mean for neighborhood equivalent to weighting (mutiplying) each cell by 1/9 = .11 (in 3x3 case) smooths the data use larger window for greater smoothing median filter use median (middle value) instead of mean smoothing, especially if data has extreme value outliers weights must sum to 1.0

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**Spatial Operations: spatial filtering -- high pass filter**

high frequency (high pass) filter negative weight filter exagerates rather than smooths local detail used for edge detection standard deviation filter (texture transform) calculate standard deviation of neighborhood raster values high SD=high texture/variability low SD=low texture/variability again used for edge detection neighorhoods spanning border have large SD ‘cos of variability cell values (vi ) on each side of edge 2 5 filtered values for highlighted pixel 1(5)(9)+5(5)(-1)+3(2)(-1) = 14 1(2)(9)+5(2)(-1)+3(5)(-1) = -7 kernel for example (wi) 1(2)(9)+8(2)(-1) = 2 1(5)(9)+8(5)(-1) = 5 fi.vi.wi

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**Spatial Operations: raster–based modelling**

Relating multiple rasters Processes may be: Local: one cell only Neighborhood: cells relating to each other in a defined manner Zonal: cells in a given section Global: all cells ArcGIS implementation: All raster analyses require either the Spatial Analyst or 3-D Analyst extensions Base ArcView can do no more than display an image (raster) data set Suitability modeling Diffusion Modeling Connectivity Modeling 1 1 for sale Site options 2 soil slope 1 1 1 1 1 3 2 System at time t+1 Incidence matrix Probability mask Initial State Connectivity matrix Resultant State

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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)

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**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

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**Attribute Operations: variable recoding**

establishing/modifying number of classes and/or their boundaries for continuous variable. Options for ArcGIS natural breaks (default) (finds inherent inherent groups via Jenks optimization which minimizes the variances within each of the classes). quantile (classes contain equal number of records-- or equal area under the frequency distribution) equal interval (user selects # of classes) (equal width classes on variable) Defined interval (user selects width of classes) standard deviation (categories based on 1,2, etc, SDs above/below mean) Manual (user defined) whole numbers (e.g. 2,000) meaningful to phenomena (e.g zero, 32o) aggregating categories on a nominal (or ordinal) variable pine and fir into evergreen No change in number of records (observations). Implement in ArcGIS via: Right click in T of C, select Properties, then Symbology tab -1 1 -2 2 34% 14% .68 -.68 23% 25% (assumes a Normal distribution) Equal area % Equal interval % Standard Deviation Equal interval score Equal area score

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**Attribute Operations: record aggregation**

combining two or more records into one, based on common values on a key variable the attribute equivalent of regionalization or classification equivalent of PROC SUMMARY in SAS interval scale variables can be aggregated using mean, sum, max, min, standard deviation, etc. as appropriate ordinal and nominal require special consideration example: aggregate county data to states, or county to CMSA Record count decreases (e.g. from 12 to 2) sum sum re-calc. count average of medians! Type of processing:

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**Attribute Operations: Joining and Relating Tables associating spatial layer to non-spatial table**

Join: one to one, or one to many, relationship appends attributes Associate table of country capitals with country layer: only one capital for each country (one to one) Associate country layer with type of government: one gov. type assigned to many countries--but each country has only one gov. type (one to many)

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**Single most common error in GIS Analysis**

--intending a one to one join of attribute to spatial table --getting a one to many join of attributes to spatial table Spatial After joining attribute to spatial data

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Attribute Operations: Joining and Relating Tables associating spatial layer to non-spatial table (contd.) Relate: many to one relationship, attributes not appended Associate country layer with its multiple cities (many to one) Note: if we flip these tables we can do a join since there is only one country for each city (one to many) For both Joins and Relates: Association exists only in the map document Underlying files not changed unless export data If joined Paris to France, for example, we lose Lyon and Marseille, therefore use relate

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**Analysis Options: Advanced & Specialized (Table of Contents)**

Proximity/point pattern analysis nearest neighbor layer distance matrix layer surface analysis cross section creation visibility/viewshed network analysis routing shortest path (2 points) travelling salesman (n points) time districting allocation Convex Hull Thiessen Polygon creation Specialized Remote Sensing image processing and classification raster modeling 3-D surface modeling spatial statistics/statistical modeling functionally specialized transportation modeling land use modeling hydrological modeling etc.

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**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 kth nearest neighbor 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 Full matrix measure location of each object relative to every other object requires output matrix with as many columns as rows in input table

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**Advanced Applications: Network Analysis**

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 Routing shortest path between two points direction instructions (locating hotel from airport) travelling salesman: shortest path connecting n points bus routing, delivery drivers 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.

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**Advanced applications: Surface Analysis**

Cross-section Drawings and Volumes elevation (or slope) values along a line Volume & cut-and-fill calculation Cross-section easy to produce for raster, more difficult for vector especially if uses contours lines Viewshed/Visibility terrain visible from a specific point applications visual impact of new construction select scenic overlooks Military Contouring Lines joining points of equal (vertical) value From raster, massed-points or breakline data Slope Transform fit a plane to the 3 by 3 neighborhood around every cell, or use a TIN output layer is the slope (first derivative) of the plane for each cell Aspect Transform direction slope faces: (E-W oriented ridge has slopes with northern and southern aspects) aspect normally classified into eight 45 degree categories calculate as horizontal component of the vector perpendicular to the surface

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**Advanced Applications: Convex Hull**

Formally: the smallest convex polygon (no concave angles) able to contain a set of points Informally: a rubber band wrapped around a set of points Just as a centroid is a point representation for a polygon, the convex hull is the polygon representation for a set of points Go to the following web site for a neat application showing how convex hull changes as you move points around No!

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**Advanced Applications: Thiessen (Dirichlet, Voronoi) Polgons and Delaunay Triangles**

Thiessen Polygons (or proximal regions or proximity polygons) polygons generated from a point layer such that any location within a polygon is closer to the enclosed point than to a point within any other polygon they divide the space between the points as ‘evenly’ as possible used for market area delimitation, rain gauge area assignment, contouring via Delaunay triangles (DTs), etc. elevation, slope and aspect of triangle calculated from heights of its three corners DTs are as near equiangular as possible and longest side is as short as possible, thus minimizes distances for interpolation A Delaunay Triangles A Thiessen neighbors of point A share a common boundary. Delauney triangles are formed by joining point to its Thiessen neighbors.

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**Specialized Applications**

Remote Sensing/Digital Image Processing reflectance value (usually 8 bit; 256 values) collected for each bands (wavelength area) in the electro-magnetic spectrum 1 band for grey scale (Black & white) 3 for color up to 200 or so for ‘hyperspectral’ permits creation of image ‘spectral signature’: set of reflectance values/ranges over available bands typifying a specific phenomena provides basis for identification of phenomena Location Science/Network Modeling Network based models for optimum location decisions for (e.g.) police beats School attendance zones Bus routes Hazardous material routing Fire station location Raster Modeling: 2-D use of direction and friction surfaces to develop models for: spread of pollution dispersion of forest fires Surface Modeling: 3-D flood potential ground water/reservoir studies Viewshed/visibility analysis Spatial Statistics/Econometrics analyses on spatial data which explicitly incorporates relative location or proximity property of observations Global (applies to entire study area) spatial autocorrelation Regressions adjusted for spatial autocorrelation Local (separately calculated for local areas) LISA (local indicators of spatial autocorrelation) Geographically weighted regression We offer one or more courses on each!

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**Implementation of Advanced and Specialized Applications in ArcGIS 8/9**

Extensions support many of the Advanced and some Specialized Applications Spatial Analyst extension provides 2-D modeling of GRID (raster) data (AV 3.2 and 8/9) 3-D Analyst extension provides 3-D modeling (AV 3.2 and 8/9) Geostatistical Analyst extension provides interpolation (ArcGis 8/9 only) Network Analyst extension (3.2 only) and ArcLogistics Route (standalone) for routing and network analysis Image Analyst extension for remote sensing applications in AV 3.2 Leica Image Analysis and Stereo Analyst for ArcGIS 8 (9 version not yet released-Fall ’04) Spatial Statistics Tools in ArcToolbox provide spatial statistics (centroid, etc..) ArcScripts support other Advanced Applications and Specialized Applications ArcScripts (in Visual Basic, C++, etc.) are used to customize ArcGIS 8 A variety of scripts available at >downloads Note: ArcScripts written in Avenue work only in ArcView 3 and will not work in ArcGIS 8/9 Many functions previously requiring Avenue scripts for AV 3.2 are built into ArcGIS 8/9 Specialized Software Packages Remote Sensing packages such as Leica GeoSystems Imagine (formerly ERDAS Imagine) For links to some of these packages go to:

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**Implementing Spatial Analysis in ArcView 3.2/3.3**

Appendix Implementing Spatial Analysis in ArcView 3.2/3.3

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**Implementing Spatial Measurement in ArcView 3.2**

Unlike in ArcGIS 8.1 where spatial measurements are provided automatically, in AV 3.2 spatial measurement often has to be implemented using Avenue code: in functional expressions in scripts Using functional expression for areas and lengths: Use Edit/Add field to add a new variable to atttributes of…table called area (or similar) Use Field/calculate and make this variable equal to: [Shape].ReturnArea Calculation is based on map units irrespective of defined distance units. If map units are feet, to obtain square miles use: [Shape].ReturnArea/5280/5280 If file is a polyline file (arcs), for length of arcs use: [Shape].ReturnLength Using a Script for areas, perimeters and lengths In Project window, select Script and click new button to open script window Use Script/load text file to load code from an existing text file e.g. arcview\samples\scripts\calcapl.ave will calculate areas, perimeters, lengths Click the “check mark” icon to compile the code. Open a View and be sure the theme you want processed is active. Click on script window then click the Runner icon to run script. variables measuring area and perimeter will be added to theme table

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**Implementing Spatial Matching in ArcView 3.2**

Available in two places (plus additional user extensions such as districting) via Theme/select by theme this selects features of the active theme which relate in some specified spatial manner to another theme if desired, selected features may be saved later to a new theme via Theme/convert to shape file via Geoprocessing Wizard Extension (use File/Extensions to load) this creates a new theme (shape file) & combines attribute tables from 2 or more input themes Six options available for different types of matching Options in Geoprocessing Wizard (use View/Geoprocessing Wizard to activate) Dissolve features based on an attribute Use for spatial aggregation/dissolving Merge themes together Use for edge matching Clip one theme based on another Use one theme to limit features in another theme (e.g. limit a Texas road theme to Dallas county only) Intersect two themes Use for polygon on polygon overlay Union two themes Assign data by location (Spatial Join) Use for: points in polygon lines in polygon points on lines (to calculate distance to nearest line) points on points (to calculate distance to “nearest neighbor” point) Scripts and extensions can provide additional capabilities Download from ArcScripts at ESRI scripts.cfm Place extensions (.avx) in your folder Arcview/ext32 The extension district.avx is good for doing spatial aggregation or “districting”

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**Using Extensions and Scripts in ArcView 3.2**

Obtain copy of script or extension Write yourself with Avenue language Supplied with ArcView in folder: arcview/samples/scripts or arcview/samples/ext Go to ArcView Help/Contents/Sample Scripts and Extensions for documentation Buy from ESRI and other companies Supplied free by ESRI or users and available on ESRI web site at: Select Avenue language or go to and click Support Be sure to print or download documentation/description To load and use an extension Place .avx file in arcview/ext32 folder Open ArcView, choose File/extensions, place tick next to name, click OK To load and use a script In Project window, select Script and click new button to open script window Use Script/load text file to load code from existing text file containing avenue code (.ave) e.g. \av_gis30\arcview\samples\scripts\calcapl.ave will calculate areas, perimeters, lengths Click the “check mark” icon to compile the code. Take steps within ArcView as appropriate for specific script e.g. Open a View and be sure the theme you want processed is active. Click on script window then click the “Runner" icon to run script. e.g. variables measuring area and perimeter will be added to theme table

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**Some Example Avenue Scripts for ArcView 3**

Avenue scripts and extensions for AV 3.2 can be downloaded from ESRI Web site to do many basic, advanced and specialized applications not available in standard products. Some examples are: Addxycoo.ave: adds X,Y coordinates of points (e.g of geocoded addresses), or of centroid for polygons, to attributes of … file Polycen.ave: creates point theme containing polygon centroids Dwizard.zip: various districting applications Use avdist31b which is an update Line.zip: enhanced buffering of lines Nearestneighbor.zip: nearest neighbor analysis For more scripts, go to: Select Avenue language

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