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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David ArcturLecturer, Research FellowUniversity of Texas at Austin

Lecture 7Feb 21, 2013Spatial Data andGeoprocessingOutlineBolstad, Ch 5, 6, 7: Data Sources, contdGPS, Aerial/Satellite Imagery, Digital DataGorr & Kurland, Ch 8: GeoprocessingAttribute extractionFeature location extractionLocation proximitiesGeoprocessing toolsModel builder2INF385T(28437) Spring 2013 Lecture 7More on Data sources: GPS, Imagery, digitalLecture 73INF385T(28437) Spring 2013 Lecture 7Measuring location & dataThree main approaches, many technologies:In situ: make field observations on site Stream flow & other gauges, GPS locationRemote sensing: observe from a distanceAerial photos, satellite sensors, LiDARModel results: products derived from working on other productsINF385T(28437) Spring 2013 Lecture 74

Global Navigation SystemsAka, Global Positioning Systems (GPS)Global Navigation Satellite Systems (GNSS)Uses WGS84 for coordinate reference system5INF385T(28437) Spring 2013 Lecture 7

Bolstad, p.1846INF385T(28437) Spring 2013 Lecture 7

GPS Ranging: get 4+ Bolstad, p.1897INF385T(28437) Spring 2013 Lecture 7

GPS Errors due to receiver sensitivity PDOP: Positional Dilution of Precision(see Bolstad, p.192)GPS: Differential CorrectionDepends on having GPS receivers with precisely known location

Differential correction can be applied in real-time or calculated later

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Bolstad, p.195Remote SensingAerial photography Satellite multispectral / hyperspectralLiDAR Light Detection and RangingSensor webs9INF385T(28437) Spring 2013 Lecture 7Bolstad, chapter 6Sensor WebsSensors connected to and discoverable on WebSensors have position & generate observationsSensor descriptions available Services to task and access sensorsLocal, regional, national scalabilityEnabling the Enterprise

WebcamEnvironmentalMonitorIndustrialProcessMonitorStoredSensorData

Traffic, BridgeMonitoringSatellite-borneImaging Device

Airborne Imaging Device

HealthMonitorStrainGaugeTempSensor

Automobileas Sensor ProbeINF385T(28437) Spring 2013 Lecture 7Source: OGC1010LiDAR Laser-based imageryHi-resolution topographyCan separate forest cover from ground layer11INF385T(28437) Spring 2013 Lecture 7

Bolstad, p.260LiDAR point clouds12INF385T(28437) Spring 2013 Lecture 7

Bolstad, p.261

LiDAR ApplicationsAgriculture yieldsBiology, conservationArchaeology beneath forest canopyGeology, soil science3D cave maps, hi-resolution beach topographyMeteorology, law enforcement, roboticsAdaptive cruise control (autos) 13INF385T(28437) Spring 2013 Lecture 7Source: WikipediaSpatial ProcessingAttribute extractionFeature location extractionLocation proximitiesGeoprocessing toolsModel builder14INF385T(28437) Spring 2013 Lecture 7Spatial Processing: Attribute extractionLecture 715INF385T(28437) Spring 2013 Lecture 7Attribute query extractionYou have tracts for an entire state, but wanttracts for one county onlyINF385T(28437) Spring 2013 Lecture 7

16Attribute query extractionSelect tracts by County FIPS ID Cook County = 03117INF385T(28437) Spring 2013 Lecture 7

Attribute query extractionCook County tractsselectedExport to new featureclass or shapefile18INF385T(28437) Spring 2013 Lecture 7

Export selected featuresRight-click to export selected features

19INF385T(28437) Spring 2013 Lecture 719Add new layerCook County tracts20INF385T(28437) Spring 2013 Lecture 7

Feature location extractionLecture 721INF385T(28437) Spring 2013 Lecture 7Select by locationPowerful function unique to GISIdentify spatial relationships between layersFinds features that are within another layer22INF385T(28437) Spring 2013 Lecture 7Select by locationHave Cook County census tracts but want City of Chicago onlyCant use Select By Attributes No attribute for Chicago

Use Municipality layerCity Chicago is a municipality within Cook County

23INF385T(28437) Spring 2013 Lecture 7Select by locationSelect Chicago from municipalities layer24INF385T(28437) Spring 2013 Lecture 7

Select by locationSelection, Select By location

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25Export selected features

26INF385T(28437) Spring 2013 Lecture 726location proximitiesLecture 727INF385T(28437) Spring 2013 Lecture 7Points near polygonsHealth officials want to know polluting companies near water features

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Points near pointsSchool officials want to know what schools are near polluting companiesINF385T(28437) Spring 2013 Lecture 7

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Polygons intersecting linesTransportation planner wants to know what neighborhoods are affected by construction project on major highwayINF385T(28437) Spring 2013 Lecture 7

30Lines intersecting polygonsPublic works official wants to know what streets or sidewalks will be affected by potential floods31INF385T(28437) Spring 2013 Lecture 7

Polygons completely within polygonsCity planners want to know what buildings are completely within a zoning area.INF385T(28437) Spring 2013 Lecture 7

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32Geoprocessing toolsLecture 733INF385T(28437) Spring 2013 Lecture 7Geoprocessing overviewGIS operations to manipulate dataTypically take input data sets, manipulate, and produce output data setsOften use multiple data sets34INF385T(28437) Spring 2013 Lecture 7Geoprocessing enables decisions

Base map fromNASA Data PoolClassify fire areas from aerialsCoordinate transformationOverlay and bufferRoads layerInternet

Data Servers (web services)

To create derived & value-added products Decision Support Client

Geoprocessing WorkflowAssess Wildfire DangerSource: OGCINF385T(28437) Spring 2013 Lecture 73535Web-based geospatial service chaining and decision support. How do we reliably and repeatedly combine results from several distributed services on the web to produce a result for a user? Service chaining is the term commonly used for the process of organizing disparate web based services into an orderly process. For instance, a raw image is sent to a service that performs a coordinate transformation. This services sends the transformed image to a classifier service that processes the image to highlight areas of active fire. The result of this service is sent to a users client along with other geospatial data such as vegetation overlays, transportation. Service chaining will play an important role in future capabilities.Common geoprocessing toolsAnalysisExtract ClipOverlay intersect and unionData managementGeneralization - dissolveGeneralAppendMerge

36INF385T(28437) Spring 2013 Lecture 7Finding the toolsGeoprocessing menu(slight differences between 10.0 and 10.1)37INF385T(28437) Spring 2013 Lecture 7

Finding the toolsArcToolbox38INF385T(28437) Spring 2013 Lecture 7

Some differences between 10.0 and 10.1 versions38Finding the toolsSearch window39INF385T(28437) Spring 2013 Lecture 7

ClipActs like a cookie cutter to create a subset of features

Input features (streets)Clip features (Central Business District)Output features (CBD streets)40INF385T(28437) Spring 2013 Lecture 7ClipINF385T(28437) Spring 2013 Lecture 7

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Some differences between 10.0 and 10.1 versions

41Clip vs. select-by-locationClip Clean edgesLooks good

Select by locationDangling edgesBetter for geocoding42INF385T(28437) Spring 2013 Lecture 7

DissolveCombines adjacent polygons to create new, larger polygonsUses common field value to remove interior lines within each polygon, forming the new polygons Aggregate (sums) data while dissolving43INF385T(28437) Spring 2013 Lecture 7DissolveCreate regions using U.S. statesUse SUB_REGION field to dissolveSum population44INF385T(28437) Spring 2013 Lecture 7

DissolveINF385T(28437) Spring 2013 Lecture 7

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Statistics Fields (optional)(may not be initially visible,scroll down to see)Dissolve results46INF385T(28437) Spring 2013 Lecture 7

States dissolved to form regionsPopulation summed for each regionAppendAppends one or more data sets into an existing data setFeatures must be of the same typeInput datasets may overlap one another and/or the target datasetTEST option: field definitions of the feature classes must be the same and in the same order for all appended featuresNO TEST option: Input features schemasdo not have to match the target feature classes' schemaINF385T(28437) Spring 2013 Lecture 747AppendDuPage and Cook County are combining public works and need a new single street centerline file.

48INF385T(28437) Spring 2013 Lecture 748AppendAppend will add DuPage streets to Cook County streetsINF385T(28437) Spring 2013 Lecture 7

49Resultant layerOne street layer (Cook County) with all records and field items

50INF385T(28437) Spring 2013 Lecture 750MergeCombines multiple input datasets of the same data type into a single, new output datasetIllinois campaign manager needs a single voting district map but wants to preserve the original layersINF385T(28437) Spring 2013 Lecture 7

51MergeINF385T(28437) Spring 2013 Lecture 7

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Resultant layerNew voting district layer

53INF385T(28437) Spring 2013 Lecture 753UnionOverlays two polygon layersResulting output layer has combined attribute data of the two inputsContains all the polygons from the inputs, whether or not they overlap

INF385T(28437) Spring 2013 Lecture 754UnionNeighborhoods and ZIP CodesINF385T(28437) Spring 2013 Lecture 7

55UnionINF385T(28437) Spring 2013 Lecture 7

56UnionBetter describes characteristics of a neighborhood.Central business district 15222 vs 15219INF385T(28437) Spring 2013 Lecture 7

57UnionAttributes tables contain different fields and data

58INF385T(28437) Spring 2013 Lecture 758Union resultsINF385T(28437) Spring 2013 Lecture 7

New polygons with combined data59

Union vs. Merge vs. Dissolve60INF385T(28437) Spring 2013 Lecture 7Operation# Input Feature ClassesChange in GeometrySchema RestrictionsUnionMultipleCombines all input geometriesIncludes all fields from all input feature classes; input tables do not have to be identicalMergeMultipleCombines all input geometriesInput tables must be identical; retains one set of attributesDissolveSingleCombines feature geometries based on shared attribute valuesN/A single feature class schemaIntersectComputes a geometric intersection of the Input FeaturesFeatures (or portions of features which overlap in all layers and/or feature classes) will be written to the Output Feature ClassInputs can have different geometry typesINF385T(28437) Spring 2013 Lecture 761IntersectCity manager needs to know what buildings intersect flood zones and wants the flood data attached to each intersecting buildingINF385T(28437) Spring 2013 Lecture 762

IntersectINF385T(28437) Spring 2013 Lecture 763

Intersect resultOnly building polygons that intersect flood zones with combined data fieldsINF385T(28437) Spring 2013 Lecture 764

Model builderLecture 865INF385T(28437) Spring 2013 Lecture 7Model builder overviewWorkflow processes can be complicatedModels automate and string functions togetherSimplifies sensitivity / parametric studies ExampleYou have census tracts for a county and want to create neighborhoods for a cityMany steps are needed to create neighborhoods (join, dissolve, etc)66INF385T(28437) Spring 2013 Lecture 7Starting mapTIGER census tracts and municipalitiesINF385T(28437) Spring 2013 Lecture 7

67Final mapTracts dissolved to create neighborhoodsINF385T(28437) Spring 2013 Lecture 7

68Crosswalk tableNeighborhood names are not included with the census tracts, so a crosswalk table was created with the name of neighborhood for each census tract

Some neighborhoods are made of multiple tractsINF385T(28437) Spring 2013 Lecture 7

69Geoprocessing optionsINF385T(28437) Spring 2013 Lecture 7

70Create a new toolboxCatalogINF385T(28437) Spring 2013 Lecture 7

71Create a new modelRight-click new ToolboxINF385T(28437) Spring 2013 Lecture 7

72Add tool to modelAdd Join ToolTo join crosswalk table to tractsINF385T(28437) Spring 2013 Lecture 7

73Set parameter for Join ToolJoins crosswalk table to census tractsINF385T(28437) Spring 2013 Lecture 7

74Model stepsINF385T(28437) Spring 2013 Lecture 7

Add Join

DissolveRemove join75Finished modelINF385T(28437) Spring 2013 Lecture 7

76SummaryBolstad, Ch 5, 6, 7: Data Sources, contdGPS, Aerial/Satellite Imagery, Digital DataGorr & Kurland, Ch 8: GeoprocessingAttribute extractionFeature location extractionLocation proximitiesGeoprocessing toolsModel builder77INF385T(28437) Spring 2013 Lecture 7