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11.520: A Workshop on
Geographic Information Systems |
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11.188: Urban Planning
and Social Science Laboratory |
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Methods of Selecting Features (within basic "vector" GIS model)
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Exploit link between map and tabular views
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Simple
Attribute Queries
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Spatial
Selection Queries
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Manipulating
and Extending Tabular Data
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Adding
Attribute Columns
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Calculating
new attribute values
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Basic
statistics on selected features
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Summary
statistics
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Using
Selected Sets
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Exporting
Data Subsets (replicating data subset)
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Using
“Definition Queries” as filters
Demo using HW1 data (1990 census tract data for Eastern Mass)
- Thematic map of median housing value (H061A001) by census tract
- msa5_tr90.shp has census tract boundaries but not census data
- msa5_tr90_data.dbf has selected census tract level data
- Must join tables before creating thematic map
- Improve visual quality of map by adjusting symbology and adding town boundaries and water layer
- Exercise ArcMap tabular data query and summarization tools
- Select-by-attribute queries (census tracts in Middlesex County)
- Definition Queries to restrict consideration of certain features
- Statistics (histogram, mean,,,) tools for selected sets
- Select-by-location queies
- ArcGIS preferences and settings:
- 'Environment' settings within ArcToolbox (right-click)
- Local vs. network locations
- Setting a local 'scratch workspace' in General Settings
Simple
Attribute Queries in ArcGIS
Basic format
<Attribute> <Operator>
<Value>
Attributes delimited
with square braces, i.e.: [Landuse]
String (text) values
typically single quote delimited
'Commercial'
Exact syntax depends on
back-end database (aargh!)
Some require
double quoted strings
Example
[Landuse]
= 'Commercial'
ArcGIS Interface Dialog perculiarity:
double click to load attribute names or values
single click for operators
Compound Attribute Queries
Remember: must repeat
the attribute name
[Landuse] = 'Commercial' or [Landuse]
= 'Industrial'
*not* [Landuse] = 'Commercial' or 'Industrial' (missing required repetition of attribute name)
*not* [Landuse] = Commercial (missing required single quotes around text)
Can build up based on
current selection set
Two pass
query: [Landuse]
= 'Commercial'
then, add to selection: [Landuse] =
'Industrial'
Fun
with Selections
By default, processing operations
occur based on selected features only
For example: to buffer
commercial land uses, first select commercial, then buffer
Subsetting based on selection
Simple, important,
poorly documented workflow
Create a selection, then
"export data" to new file
(Sorry no cut and
paste!)
In the attribute table, calculations
done only on selected features
Useful for calculating
new attributes, often for reclassification/aggregation
Example: ranking store
location suitability based on zoning layer
Logic: best = commercial or mixed use
moderate = industrial
worst = residential
Strategy:
Create
new rating attribute [rating] in zoning table
Select
best features, calculate rating attribute = 'best'
Select
moderate features, cal rating 'moderate'
etc.
Advantages/Disadvantages:
Permanent
change to database, ranking result obvious
Method *not*
obvious after the fact (requires external documentation)
Single,
transferable data set
Requires
"write" permission on the database
Spatial Selections
(This capability is different from
other textual databases)
Can select features
based on their spatial relationship with other selected features
You will need to do this for your
homework
Intro
to Geoprocessing
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Data
Models Vary in Degree of
§ Geometric refinement
· What’s the MMU (minimum mapping
unit)?
How are contiguous features
segmented?
§ Attribute Refinement
· How many classes of land use are
recorded? (urban/suburban or 27 types?)
· Are the aspects you need directly
coded at all? (traffic congestion, historic building
quality?)
§ Temporal Refinement
· How up to date are your data?
· Are all layers in temporal synch?
· Is your question about current
conditions, or really about future conditions?
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Common
cases when your data model doesn’t match your question
Example: Classify shopping centers into five classes based on square feet
Example: Reclassify 27 land use types into built/unbuilt
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Spatial
aggregation and disaggregation require more than
simple selection – require creating new geometries based on combinations of
existing geometries
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Some
Useful and Common Geoprocessing Operations
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Spatial
data subsetting using “Clip”
§ Selects those features within a
polygonal geometry, breaking partially included features as needed
o Buffering
§ Creates new geometry representing an area within a given distance from selected features
§ By default creates one buffered object per feature. Often useful to “join” output geometries
Example: Site Selection for Low Cost
Grocery Store Chain
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Conceptual
Model
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Brainstorm
Criteria for "good" locations
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Case
Study Example
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Factors
used in
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Powerpoint slides used by commercial firm to market site selection tools
by Edens & Avant and RPM consulting