Transcript lecture 7 ppt
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 INF385T(28437) – Spring 2013 – Lecture 7 2
Lecture 7
MORE ON DATA SOURCES: GPS, IMAGERY, DIGITAL
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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 Bolstad, p.184
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GPS Ranging: get 4+
Bolstad, p.189
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GPS Errors due to receiver sensitivity
PDOP: Positional Dilution of Precision (see Bolstad, p.192) INF385T(28437) – Spring 2013 – Lecture 7 7
GPS: Differential Correction
Depends on having GPS receivers with precisely known location Differential correction can be applied in real-time or calculated later Bolstad, p.195
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Remote Sensing
Aerial photography Satellite multispectral / hyperspectral LiDAR – Light Detection and Ranging Sensor webs Bolstad, chapter 6 INF385T(28437) – Spring 2013 – Lecture 7 9
Sensor Webs Industrial Process Monitor
Sensors connected to and discoverable on Web Sensors have position & generate observations Sensor descriptions available Services to task and access sensors
Automobile as Sensor Probe
Local, regional, national scalability Enabling the Enterprise
Airborne Imaging Device Traffic, Bridge Monitoring
Temp Sensor
Stored Sensor Data Environmental Monitor Webcam Satellite-borne
Source: OGC
Imaging Device
Strain Gauge
Health Monitor
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LiDAR – Laser-based imagery
Hi-resolution topography Can separate forest cover from ground layer Bolstad, p.260
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LiDAR point clouds
Bolstad, p.261
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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) Source: Wikipedia INF385T(28437) – Spring 2013 – Lecture 7 13
Spatial Processing
Attribute extraction Feature location extraction Location proximities Geoprocessing tools Model builder INF385T(28437) – Spring 2013 – Lecture 7 14
Lecture 7
SPATIAL PROCESSING: ATTRIBUTE EXTRACTION
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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 = 031 INF385T(28437) – Spring 2013 – Lecture 7 17
Attribute query extraction
Cook County tracts selected Export to new feature class or shapefile INF385T(28437) – Spring 2013 – Lecture 7 18
Export selected features
Right-click to export selected features INF385T(28437) – Spring 2013 – Lecture 7 19
Add new layer
Cook County tracts INF385T(28437) – Spring 2013 – Lecture 7 20
Lecture 7
FEATURE LOCATION EXTRACTION
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Select by location
Powerful function unique to GIS Identify spatial relationships between layers Finds features that are within another layer INF385T(28437) – Spring 2013 – Lecture 7 22
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 INF385T(28437) – Spring 2013 – Lecture 7 23
Select by location
Select “Chicago” from municipalities layer INF385T(28437) – Spring 2013 – Lecture 7 24
Select by location
Selection, Select By location INF385T(28437) – Spring 2013 – Lecture 7 25
Export selected features
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Lecture 7
LOCATION PROXIMITIES
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Points near polygons
Health officials want to know polluting companies near water features INF385T(28437) – Spring 2013 – Lecture 7 28
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 INF385T(28437) – Spring 2013 – Lecture 7 31
Polygons completely within polygons
City planners want to know what buildings are completely within a zoning area.
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Lecture 7
GEOPROCESSING TOOLS
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Geoprocessing overview
GIS operations to manipulate data Typically take input data sets, manipulate, and produce output data sets Often use multiple data sets INF385T(28437) – Spring 2013 – Lecture 7 34
Geoprocessing enables decisions
Assess Wildfire Danger
Geoprocessing Workflow
To create derived & value-added products
Decision Support Client Internet Base map from NASA Data Pool Coordinate transformation Classify fire areas from aerials
…
Overlay and buffer Roads layer
Source: OGC
Data Servers (web services)
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Common geoprocessing tools
Analysis Extract – Clip Overlay – intersect and union Data management Generalization - dissolve General Append Merge INF385T(28437) – Spring 2013 – Lecture 7 36
Finding the tools
Geoprocessing menu (slight differences between 10.0 and 10.1) INF385T(28437) – Spring 2013 – Lecture 7 37
Finding the tools
ArcToolbox INF385T(28437) – Spring 2013 – Lecture 7 38
Finding the tools
Search window INF385T(28437) – Spring 2013 – Lecture 7 39
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
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Clip vs. select-by-location
Clip Clean edges Looks good Select by location Dangling edges Better for geocoding INF385T(28437) – Spring 2013 – Lecture 7 42
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 INF385T(28437) – Spring 2013 – Lecture 7 43
Dissolve
Create regions using U.S. states Use SUB_REGION field to dissolve Sum population INF385T(28437) – Spring 2013 – Lecture 7 44
Dissolve
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Statistics Fields (optional)
(may not be initially visible, scroll down to see)
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Dissolve results
States dissolved to form regions Population summed for each region INF385T(28437) – Spring 2013 – Lecture 7 46
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.
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Append
Append will
add
County streets DuPage streets
to
Cook INF385T(28437) – Spring 2013 – Lecture 7 49
Resultant layer
One street layer (Cook County) with all records and field items INF385T(28437) – Spring 2013 – Lecture 7 50
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
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Resultant layer
New voting district layer INF385T(28437) – Spring 2013 – Lecture 7 53
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
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Union
Better describes characteristics of a neighborhood.
Central business district 15222 vs 15219 INF385T(28437) – Spring 2013 – Lecture 7 57
Union
Attributes tables contain different fields and data 58 INF385T(28437) – Spring 2013 – Lecture 7
Union results
New polygons with combined data INF385T(28437) – Spring 2013 – Lecture 7 59
Union vs. Merge vs. Dissolve Operation Union # Input Feature Classes
Multiple
Merge Dissolve
Multiple Single
Change in Geometry Schema Restrictions
Combines all input geometries Combines all input geometries Combines feature geometries based on shared attribute values Includes all fields from all input feature classes; input tables do not have to be identical Input tables must be identical; retains one set of attributes N/A – single feature class schema 60 INF385T(28437) – Spring 2013 – Lecture 7
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
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Intersect result
Only building polygons that intersect flood zones with combined data fields INF385T(28437) – Spring 2013 – Lecture 7 64
Lecture 8
MODEL BUILDER
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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
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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
Add Join Dissolve Remove join INF385T(28437) – Spring 2013 – Lecture 7 75
Finished model
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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 INF385T(28437) – Spring 2013 – Lecture 7 77