Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow University of Texas at Austin Lecture 7 Feb 21, 2013 Spatial Data and Geoprocessing

Outline  Bolstad, Ch 5, 6, 7: Data Sources, cont’d  GPS, Aerial/Satellite Imagery, Digital Data  Gorr & Kurland, Ch 8: Geoprocessing  Attribute extraction  Feature location extraction  Location proximities  Geoprocessing tools  Model builder 2 INF385T(28437) – Spring 2013 – Lecture 7

MORE ON DATA SOURCES: GPS, IMAGERY, DIGITAL Lecture 7 3 INF385T(28437) – Spring 2013 – Lecture 7

Measuring location & data  Three main approaches, many technologies:  In situ: make field observations on site  Stream flow & other gauges, GPS location  Remote sensing: observe from a distance  Aerial photos, satellite sensors, LiDAR  Model results: products derived from working on other products INF385T(28437) – Spring 2013 – Lecture 7 4

Global Navigation Systems Aka,  Global Positioning Systems (GPS)  Global Navigation Satellite Systems (GNSS) Uses WGS84 for coordinate reference system 5 INF385T(28437) – Spring 2013 – Lecture 7 Bolstad, p.184

6 INF385T(28437) – Spring 2013 – Lecture 7 GPS Ranging: get 4+ Bolstad, p.189

7 INF385T(28437) – Spring 2013 – Lecture 7 GPS Errors due to receiver sensitivity PDOP: Positional Dilution of Precision (see Bolstad, p.192)

GPS: Differential Correction  Depends on having GPS receivers with precisely known location  Differential correction can be applied in real-time or calculated later 8 INF385T(28437) – Spring 2013 – Lecture 7 Bolstad, p.195

Remote Sensing  Aerial photography  Satellite multispectral / hyperspectral  LiDAR – Light Detection and Ranging  Sensor webs 9 INF385T(28437) – Spring 2013 – Lecture 7 Bolstad, chapter 6

Sensor Webs  Sensors connected to and discoverable on Web  Sensors have position & generate observations  Sensor descriptions available  Services to task and access sensors  Local, regional, national scalability  Enabling the Enterprise Webcam Environmental Monitor Industrial Process Monitor Stored Sensor Data Traffic, Bridge Monitoring Satellite-borne Imaging Device Airborne Imaging Device Health Monitor Strain Gauge Temp Sensor Automobile as Sensor Probe INF385T(28437) – Spring 2013 – Lecture 7 Source: OGC 10

LiDAR – Laser-based imagery  Hi-resolution topography  Can separate forest cover from ground layer 11 INF385T(28437) – Spring 2013 – Lecture 7 Bolstad, p.260

LiDAR point clouds 12 INF385T(28437) – Spring 2013 – Lecture 7 Bolstad, p.261

LiDAR Applications  Agriculture yields  Biology, conservation  Archaeology beneath forest canopy  Geology, soil science  3D cave maps, hi-resolution beach topography  Meteorology, law enforcement, robotics  Adaptive cruise control (autos) 13 INF385T(28437) – Spring 2013 – Lecture 7 Source: Wikipedia

Spatial Processing  Attribute extraction  Feature location extraction  Location proximities  Geoprocessing tools  Model builder 14 INF385T(28437) – Spring 2013 – Lecture 7

SPATIAL PROCESSING: ATTRIBUTE EXTRACTION Lecture 7 15 INF385T(28437) – Spring 2013 – Lecture 7

Attribute query extraction You have tracts for an entire state, but want tracts for one county only INF385T(28437) – Spring 2013 – Lecture 7 16

Attribute query extraction  Select tracts by County FIPS ID  Cook County = INF385T(28437) – Spring 2013 – Lecture 7

Attribute query extraction  Cook County tracts selected  Export to new feature class or shapefile 18 INF385T(28437) – Spring 2013 – Lecture 7

Export selected features  Right-click to export selected features 19 INF385T(28437) – Spring 2013 – Lecture 7

Add new layer  Cook County tracts 20 INF385T(28437) – Spring 2013 – Lecture 7

FEATURE LOCATION EXTRACTION Lecture 7 21 INF385T(28437) – Spring 2013 – Lecture 7

Select by location  Powerful function unique to GIS  Identify spatial relationships between layers  Finds features that are within another layer 22 INF385T(28437) – Spring 2013 – Lecture 7

Select by location  Have Cook County census tracts but want City of Chicago only  Can’t use Select By Attributes  No attribute for Chicago  Use “Municipality” layer  City Chicago is a municipality within Cook County 23 INF385T(28437) – Spring 2013 – Lecture 7

Select by location  Select “Chicago” from municipalities layer 24 INF385T(28437) – Spring 2013 – Lecture 7

Select by location  Selection, Select By location 25 INF385T(28437) – Spring 2013 – Lecture 7

Export selected features 26 INF385T(28437) – Spring 2013 – Lecture 7

LOCATION PROXIMITIES Lecture 7 27 INF385T(28437) – Spring 2013 – Lecture 7

Points near polygons  Health officials want to know polluting companies near water features 28 INF385T(28437) – Spring 2013 – Lecture 7

Points near points  School officials want to know what schools are near polluting companies INF385T(28437) – Spring 2013 – Lecture 7 29

Polygons intersecting lines  Transportation planner wants to know what neighborhoods are affected by construction project on major highway INF385T(28437) – Spring 2013 – Lecture 7 30

Lines intersecting polygons  Public works official wants to know what streets or sidewalks will be affected by potential floods 31 INF385T(28437) – Spring 2013 – Lecture 7

Polygons completely within polygons  City planners want to know what buildings are completely within a zoning area. INF385T(28437) – Spring 2013 – Lecture 7 32

GEOPROCESSING TOOLS Lecture 7 33 INF385T(28437) – Spring 2013 – Lecture 7

Geoprocessing overview  GIS operations to manipulate data  Typically take input data sets, manipulate, and produce output data sets  Often use multiple data sets 34 INF385T(28437) – Spring 2013 – Lecture 7

Geoprocessing enables decisions … Base map from NASA Data Pool Classify fire areas from aerials Coordinate transformation Overlay and buffer Roads layer Internet Data Servers (web services) To create derived & value-added products Decision Support Client Geoprocessing Workflow Assess Wildfire Danger Source: OGC INF385T(28437) – Spring 2013 – Lecture 7 35

Common geoprocessing tools  Analysis  Extract – Clip  Overlay – intersect and union  Data management  Generalization - dissolve  General  Append  Merge 36 INF385T(28437) – Spring 2013 – Lecture 7

Finding the tools  Geoprocessing menu (slight differences between 10.0 and 10.1) 37 INF385T(28437) – Spring 2013 – Lecture 7

Finding the tools  ArcToolbox 38 INF385T(28437) – Spring 2013 – Lecture 7

Finding the tools  Search window 39 INF385T(28437) – Spring 2013 – Lecture 7

Clip  Acts like a “cookie cutter” to create a subset of features Input features (streets) Clip features (Central Business District) Output features (CBD streets) 40 INF385T(28437) – Spring 2013 – Lecture 7

Clip INF385T(28437) – Spring 2013 – Lecture 7 41

Clip vs. select-by-location  Clip  Clean edges  Looks good  Select by location  Dangling edges  Better for geocoding 42 INF385T(28437) – Spring 2013 – Lecture 7

Dissolve  Combines adjacent polygons to create new, larger polygons  Uses common field value to remove interior lines within each polygon, forming the new polygons  Aggregate (sums) data while dissolving 43 INF385T(28437) – Spring 2013 – Lecture 7

Dissolve  Create regions using U.S. states  Use SUB_REGION field to dissolve  Sum population 44 INF385T(28437) – Spring 2013 – Lecture 7

Dissolve INF385T(28437) – Spring 2013 – Lecture 7 45 Statistics Fields (optional) (may not be initially visible, scroll down to see)

Dissolve results 46 INF385T(28437) – Spring 2013 – Lecture 7  States dissolved to form regions  Population summed for each region

Append  Appends one or more data sets into an existing data set  Features must be of the same type  Input datasets may overlap one another and/or the target dataset  TEST option: field definitions of the feature classes must be the same and in the same order for all appended features  NO TEST option: Input features schemas do not have to match the target feature classes' schema INF385T(28437) – Spring 2013 – Lecture 7 47

Append  DuPage and Cook County are combining public works and need a new single street centerline file. 48 INF385T(28437) – Spring 2013 – Lecture 7

Append  Append will add DuPage streets to Cook County streets INF385T(28437) – Spring 2013 – Lecture 7 49

Resultant layer  One street layer (Cook County) with all records and field items 50 INF385T(28437) – Spring 2013 – Lecture 7

Merge  Combines multiple input datasets of the same data type into a single, new output dataset  Illinois campaign manager needs a single voting district map but wants to preserve the original layers INF385T(28437) – Spring 2013 – Lecture 7 51

Merge INF385T(28437) – Spring 2013 – Lecture 7 52

Resultant layer  New voting district layer 53 INF385T(28437) – Spring 2013 – Lecture 7

Union  Overlays two polygon layers  Resulting output layer has combined attribute data of the two inputs  Contains all the polygons from the inputs, whether or not they overlap INF385T(28437) – Spring 2013 – Lecture 7 54

Union  Neighborhoods and ZIP Codes INF385T(28437) – Spring 2013 – Lecture 7 55

Union INF385T(28437) – Spring 2013 – Lecture 7 56

Union  Better describes characteristics of a neighborhood.  Central business district vs INF385T(28437) – Spring 2013 – Lecture 7 57

Union  Attributes tables contain different fields and data 58 INF385T(28437) – Spring 2013 – Lecture 7

Union results INF385T(28437) – Spring 2013 – Lecture 7  New polygons with combined data 59

Union vs. Merge vs. Dissolve 60 INF385T(28437) – Spring 2013 – Lecture 7 Operation# Input Feature Classes Change in Geometry Schema Restrictions UnionMultipleCombines all input geometries Includes all fields from all input feature classes; input tables do not have to be identical MergeMultipleCombines all input geometries Input tables must be identical; retains one set of attributes DissolveSingleCombines feature geometries based on shared attribute values N/A – single feature class schema

Intersect  Computes a geometric intersection of the Input Features  Features (or portions of features which overlap in all layers and/or feature classes) will be written to the Output Feature Class  Inputs can have different geometry types INF385T(28437) – Spring 2013 – Lecture 7 61

Intersect  City manager needs to know what buildings intersect flood zones and wants the flood data attached to each intersecting building INF385T(28437) – Spring 2013 – Lecture 7 62

Intersect INF385T(28437) – Spring 2013 – Lecture 7 63

Intersect result  Only building polygons that intersect flood zones with combined data fields INF385T(28437) – Spring 2013 – Lecture 7 64

MODEL BUILDER Lecture 8 65 INF385T(28437) – Spring 2013 – Lecture 7

Model builder overview  Workflow processes can be complicated  Models automate and string functions together  Simplifies sensitivity / parametric studies  Example  You have census tracts for a county and want to create neighborhoods for a city  Many steps are needed to create neighborhoods (join, dissolve, etc) 66 INF385T(28437) – Spring 2013 – Lecture 7

Starting map  TIGER census tracts and municipalities INF385T(28437) – Spring 2013 – Lecture 7 67

Final map  Tracts dissolved to create neighborhoods INF385T(28437) – Spring 2013 – Lecture 7 68

Crosswalk table  Neighborhood 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 tracts INF385T(28437) – Spring 2013 – Lecture 7 69

Geoprocessing options INF385T(28437) – Spring 2013 – Lecture 7 70

Create a new toolbox  Catalog INF385T(28437) – Spring 2013 – Lecture 7 71

Create a new model  Right-click new Toolbox INF385T(28437) – Spring 2013 – Lecture 7 72

Add tool to model  Add Join Tool  To join crosswalk table to tracts… INF385T(28437) – Spring 2013 – Lecture 7 73

Set parameter for Join Tool  Joins crosswalk table to census tracts INF385T(28437) – Spring 2013 – Lecture 7 74

Model steps INF385T(28437) – Spring 2013 – Lecture 7  Add Join  Dissolve  Remove join 75

Finished model INF385T(28437) – Spring 2013 – Lecture 7 76

Summary  Bolstad, Ch 5, 6, 7: Data Sources, cont’d  GPS, Aerial/Satellite Imagery, Digital Data  Gorr & Kurland, Ch 8: Geoprocessing  Attribute extraction  Feature location extraction  Location proximities  Geoprocessing tools  Model builder 77 INF385T(28437) – Spring 2013 – Lecture 7