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Analysis in GIS Analysis Components analyses may be applied to (use as input): – tabular attribute data – spatial data/layers – combination of spatial and tabular results may be displayed as (produce as output): – table subsets, table combinations, highlighted records (rows), new variables (columns) – charts – maps/map features: highlights on existing themes new themes/layers – combination Availability of Analytical Capabilities: Analysis Options Basic: Desktop GIS packages – ArcGIS ArcView – Mapinfo – Geomedia (Bentley); Geographics (Intergraph) Advanced: Professional GIS systems – ArcGIS ArcINFO, Intergraph MGE – provide data editing plus more advanced analysis Capabilities move Specialized: modelling and simulation ‘up the chain’ – via scripting/programming within GIS over time. ArcObjects in ArcGIS – via specialized packages and/or GISs 3-D Scientific Visualization packages transportation planning packages ER Mapper, PCI for raster Advanced and Specialized Applications: in comparison to basic applications Most ‘basic’ analyses are used to create descriptive models of the world, that is, representations of reality as it exists. Most ‘advanced’ analyses involve creating a new conceptual output layer, or in some cases table(s) or chart(s), the values of which are some transformation of the values in the descriptive input layer. e.g. slope or aspect layer Most ‘specialized’ applications involve 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. fire spread model, traffic projections Analysis Options: Basic Spatial Operations Attribute Operations – centroid – record selection determination tabular via SQL – spatial measurement ‘information – buffer analysis clicking’ with – neighborhood cursor analysis/spatial – variable recoding filtering – record aggregation – geocoding – general statistical – polygon overlay analysis – spatial aggregation redistricting regionalization classification Analysis Options: Advanced & Specialized Advanced Specialized surface analysis Remote Sensing image – cross section creation processing and classification – visibility/viewshed raster modelling proximity analysis 3-D surface modelling – nearest neighbor layer spatial – distance matrix layer statistics/statistical network analysis modelling – routing functionally specialized shortest path (2 points) – transportation traveling salesman (n modelling points) – land use modelling – time districting – hydrological modelling – allocation – etc. Thiessen Polygon creation Spatial operations: Centroid or Mean Center n n X i Y i balancing point for a spatial distribution X i 1 ,Y i 1 n n – point representation for a polygon--analogous to the mean – single point summary for a distribution (point or polygon) useful for – summarizing change over time in a distribution (e.g Canadian pop. centroid every 10 years) – placing labels for polygons for weird-shaped polygons, centroid may not lie within polygon centroid outside polygon Spatial operations: Spatial Measurement Spatial measurements: Comments: possible distance metrics: distance measures – straight line/airline – between points – city block/manhattan – from point or raster to metric polygon or zone – distance thru network boundary – time/friction thru network – between polygon shape often measured by: centroids perimeter = 1.0 for circle polygon area area x 3.54 = 1.13 for square = large number for irregular polygon polygon perimeter may generate output raster: polygon shape – value of spatial volume calculation measurement assigned to – e.g. for earth moving, each pixel within a polygon resevoirs or zone direction determination Projection affects values!!! – e.g. for smoke plumes Spatial operations: Spatial Measurement--example AREA PERIMETER CNTY_ CNTY_ID NAME FIPS 0.265 2.729 2605 2605 Anderson 48001 0.368 2.564 2545 2545 Andrews 48003 0.209 2.171 2680 2680 Angelina 48005 0.072 2.642 2899 2899 Aransas 48007 0.233 1.941 2335 2335 Archer 48009 0.233 1.941 2103 2103 Armstrong 48011 0.299 2.278 2870 2870 Atascosa 48013 0.159 2.115 2830 2830 Austin 48015 0.203 1.806 2256 2256 Bailey 48017 0.205 2.114 2844 2844 Bandera 48019 0.218 1.842 2801 2801 Bastrop 48021 0.223 1.897 2338 2338 Baylor 48023 Attributes of ..... file in ArcView provides area and perimeter measurements automatically. Spatial Operations: buffer zones polygon buffer Buffer zones region within ‘x’ distance Examples units 200 foot buffer around buffer any object: point, property where zoning line or polygon change requested use multiple buffers at 100 ft buffer from stream progressively greater center line limiting distances to show gradation development may define a ‘friction’ or 3 mile zone beyond city ‘cost’ layer so that spread is boundary showing ETJ not linear with distance (extra territorial point jurisdiction) buffers line use to define (or exclude) buffer areas as options (e.g for retail site) or for further analysis in conjunction with ‘friction layer’, simulate spread of fire Spatial Operations: neighborhood analysis/spatial filtering spatial convolution or filter low frequency ( low pass) filter: – value of each cell replaced mean filter by some function of the – cell replaced by the mean values of itself and the for neighborhood cells (or polygons) surrounding it – equivalent to weighting (mutiplying) each cell by – can use ‘neighborhood’ or 1/9 = .11 (in 3x3 case) ‘window’ of any size – smooths the data 3x3 cells (8-connected) – use larger window for 5x5, 7x7, etc. greater smoothing – differentially weight the median filter cells to produce different effects – use median (middle value) instead of mean – kernel for 3x3 mean filter: – smoothing, especially if data 1/9 1/9 1/9 has extreme value outliers 1/9 1/9 1/9 weights must 1/9 1/9 1/9 sum to 1.0 Spatial Operations: Polygon Overlay combines two (or more) Examples layers to create a third assign environmental samples used to integrate attribute (points) to census tracts to data having different spatial estimate exposure per capita properties (point v. polygon) (polygon on point) or boundaries (zip and tract) identify tracts traversed by can overlay polygons on: freeway for study of – points (point in polygon) neighborhood blight (polygon – lines (line on polygon) on lines) – other polygons integrate census data by many different Boolean logic block with sales data by zip combinations possible code (polygon on polygon) – Union (A or B) – Intersection (A and B) – A and not B ; not (A and B) Spatial Matching via Polygon Overlay: example Land Use a. b. c. The two themes (land use & drainage basins) do not Atlantic Drainage A. have common Gulf G. boundaries. GIS creates Basins combined layer permitting calculation of Combined layer aA bA cA land use by drainage aG bG cG basin. Point-point is within , e.g. find all of the customer points within 1 km of this retail store point is nearest to , e.g. find the hazardous waste site which is nearest to this groundwater well Point-line ends at , e.g. find the intersection at the end of this street is nearest to , e.g. find the road nearest to this aircraft crash site Point-area is contained in , e.g. find all of the customers located in this ZIP code boundary can be seen from , e.g. determine if any of this lake can be seen from this viewpoint Line-line crosses , e.g. determine if this road crosses this river comes within , e.g. find all of the roads which come within 1 km of this railroad flows into , e.g. find out if this stream flows into this river Line-area crosses , e.g. find all of the soil types crossed by this railroad borders , e.g. find out if this road forms part of the boundary of this airfield Area-area overlaps , e.g. identify all overlaps between types of soil on this map and types of land use on this other map is nearest to , e.g. find the nearest lake to this forest fire is adjacent to , e.g. find out if these two areas share a common boundary Spatial Operations: Geocoding assigning spatial coordinates (explict location) to addresses (implicit location) usually assigns x,y coordinates; could be lat/long requires street network file with street attribute information (street name and number range for each block) for all street segments (blocks) precise matching of street names can be problemmatic – completeness (esp. for ‘new’ streets) important – PO boxes, building names, and apartment complex names cause problems. 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 Spatial Operations: spatial aggregation (dissolving) Groupings may be: formal (based on in situ Examples: characteristics) e.g. city neighborhoods elementary school zones to high functional (based on flows or school attendance zones links): (functional districting) e.g. commuting zones districting/redistricting election precincts (or city blocks) – grouping contiguous polygons into legislative districts (formal into districts districting) – original polygons preserved creating police precincts (funct. regionalization reg.) – grouping polygons into contiguous regions creating city neighborhood map – original polygon boundaries (form. reg.) dissolved grouping census tracts into classification market segments--yuppies, – grouping polygons into non- nerds, etc (class.) contiguous regions – original boundaries usually creating soils or zoning map dissolved (class) – usually ‘formal’ groupings Advanced Applications: Network Analysis Routing Districting expand from site along network shortest path between until criteria (time, distance, cost, two points object count) is reached; then – direction instructions assign area to district (locating hotel from – creating market areas, airport) attendance zones, etc Allocation travelling salesman: assign locations to the nearest shortest path connecting center n points – assign customers to pizza – bus routing, delivery delivery outlets drivers draw boundaries (lines of In all cases, ‘distance’ may be equidistance) based on the above measured in miles, time, cost or – market area creation other ‘friction’ (e.g pipe diameter – example of application of for water, sewage, etc.). Thiessen polygons Arc or node attributes (e.g one-way streets, no left turn) may also be critical.
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