1 8/19/2014 ron briggs, utdallas gisc 6381 gis fundamentals analysis and modeling in gis

104/11/23 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals

Analysis and Modeling in GIS


GIS and the Levels of ScienceDescription:Using GIS to create descriptive models of the world

--representations of reality as it exists.

Analysis:Using GIS to answer a question or test an hypothesis.Often involves creating a new conceptual output layer, (or table or chart),

the values of which are some transformation of the values in the descriptive input layer.

--e.g. buffer or slope or aspect layers

Prediction: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. flood models, fire spread models, urban growth models


The Analysis Challenge• Recognizing which generic GIS analytic capability (or

combination) can be used to solve your problem:– meet an operational need – answer a question posed by your boss or your board– address a scientific issue and/or test a hypothesis

Send mailings to property owners potentially affected by a proposed change in zoning

Determine if a crime occurred within a school’s “drug free zone” Determine the acreage of agricultural, residential, commercial and

industrial land which will be lost by construction of new highway corridorDetermine the proportion of a region covered by igneous extrusionsDo Magnitude 4 or greater sub-oceanic earthquakes occur closer to the

Pacific coast of South America than of North America?Are gas stations or fast food joints closer to freeways?

Availability of Capabilities in GIS Software• Descriptive Focus: Basic Desktop GIS packages

– Data editing, description and basic analysis – ArcView– Mapinfo– Geomedia

• Analytic Focus: Advanced Professional GIS systems – More sophisticated data editing plus more advanced analysis– ARC/INFO, MapInfo Pro, etc.Provided through extra cost Extensions

or professional versions of desktop packages

• Prediction: Specialized modeling and simulation – via scripting/programming within GIS

» VB and ArcObjects in ArcGIS » Avenue scripts in ArcView 3.2» AMLs in Workstation ARC/INFO (v. 7) Write your own or download from ESRI Web site

– via specialized packages and/or GISs» 3-D Scientific Visualization packages» transportation planning packages e.g TransCAD» ERDAS, ER Mapper or similar package for raster

Capabilities move

‘down the chain’

over time.

In earlier generation GIS systems, use of advanced applications often required learning another package with a different user interface and operating system (usually UNIX).


Description and Basic Analysis(Table of Contents)

• Spatial OperationsVector– spatial measurement– Centrographic statistics– buffer analysis– spatial aggregation

» redistricting» regionalization» classification

– Spatial overlays and joins Raster– neighborhood

analysis/spatial filtering– Raster modeling

• Attribute Operations– record selection

» tabular via SQL

» ‘information clicking’ with cursor

– variable recoding

– record aggregation

– general statistical analysis

– table relates and joins


Spatial measurements:• distance measures

– between points– from point or raster

to polygon or zone boundary– between polygon centroids

• polygon area• polygon perimeter• polygon shape• volume calculation

– e.g. for earth moving, reservoirs• direction determination

– e.g. for smoke plumes

Spatial operations: Spatial Measurement Comments:• Cartesian distance via Pythagorus

Used for projected data by ArcMap measure tools• Spherical distance via spherical coordinates

Cos d = (sin a sin b) + (cos a cos b cos P) where: d = arc distance a = Latitude of A

b = Latitude of BP = degrees of long. A to B

Used for unprojected data by ArcMap measure tools• possible distance metrics:

– straight line/airline– city block/manhattan metric– distance thru network– time/friction thru network

• shape often measured by:

• Projection affects values!!!

perimeter

area x 3.54

= 1.0 for circle= 1.13 for squareLarge for complex shape

ArcGIS geodatabases contain automatic variables:shape.length: line length or

polygon perimetershape.area: polygon areaAutomatically updated after editing.For shapefiles, these must be calculated e.g. by opening attribute table and applying Calculate Geometry to a column (AV 9.2)

Distances depend on projection. Perimeter to area ratio differs

22 )()( jijiij YYXXd


Spatial operations: Spatial MeasurementSHAPE AREA PERIMETER CNTY_ CNTY_ID NAME FIPS Shape IndexPolygon 0.265 2.729 2605 2605 Anderson 48001 1.50Polygon 0.368 2.564 2545 2545 Andrews 48003 1.19Polygon 0.209 2.171 2680 2680 Angelina 48005 1.34Polygon 0.072 2.642 2899 2899 Aransas 48007 2.78Polygon 0.233 1.941 2335 2335 Archer 48009 1.14Polygon 0.233 1.941 2103 2103 Armstrong 48011 1.14Polygon 0.299 2.278 2870 2870 Atascosa 48013 1.18PolygonPolygon 0.224 1.900 2471 2471 Dallas 48113 1.13Polygon 0.222 1.889 2481 2481 Dawson 48115 1.13Polygon 0.368 2.580 2106 2106 Deaf Smith 48117 1.20Polygon 0.072 1.421 2386 2386 Delta 48119 1.50

Area and Perimeter measures are automatically maintained in the attributes table for a Geodatabase or coverage. For a shapefile, you need to apply Calculate Geometry to an appropriate column in the attribute table (or convert to a geodatabase) .

The shape index can be calculated from the area and perimeter measurements. (Note: shapefile and shape index are unrelated)


Spatial Measurement: Calculating the Area of a Polygon

)/2YY ( )X-(X 21121i

n

0 105

010

5

0 105

010

5

2,3

7,7

7,3

6,2

4,7

Area=(2 x 4)/2=4

Area=(3 x 4)=12

Area=(5 x 1)/2=2.5

5

0 105

5

0 105

=

-

A CB

=

-

The actual algorithm used obtains the area of A by calculating the areas of B and C, and then subtracting.The actual formulae used is as follows:

i X Y X2-X1 (Y1+Y2)/2 product sum

1 2 3 2 5 10 102 4 7 3 7 21 313 7 7 0 5 0 314 7 3 -1 2.5 -2.5 28.55 6 2 -4 2.5 -10 18.5

2 3

Its implementation in Excel is shown below.

The area of the above polygon is 18.5, based on dividing it into rectangles and triangles. However, this is not practical for a complex polygon.Area of triangle =

(base x height)/2


Spatial Operations:

Centrographic Statistics• Basic descriptors for spatial point distributions • Two dimensional (spatial) equivalents of standard descriptive statistics

(mean, standard deviation) for a single-variable distributionMeasures of Centrality (equivalent to mean)– Mean Center and CentroidMeasures of Dispersion (equivalent to standard deviation or variance)– Standard Distance– Standard Deviational Ellipse

• Can be applied to polygons by first obtaining the centroid of each polygon

• Best used in a comparative context to compare one distribution (say in 1990, or for males) with another (say in 2000, or for females)


Centroid and Mean Center• balancing point for a spatial distribution

– analogous to the mean– single point representation for a polygon (centroid)– single point summary for a point distribution (mean center)– can be weighted by ‘magnitude’ at each point (analogous to weighted mean)– minimizes squared distances to other points, thus ‘distant’ points have bigger

influence than close points ( Oregon births more impact than Kansas births!)– is not the point of “minimum aggregate travel”--this would minimize distances (not

their square) and can only be identified by approximation.

• useful for – summarizing change over time in a distribution (e.g US pop. centroid every 10 years)– placing labels for polygons

• for weird-shaped polygons, centroid may not lie within polygon centroid outside

polygon

n

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n

XX

n

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Note: many ArcView applications calculate only a “psuedo” centroid: the coordinates of the bounding box (the extent) of the polygon

Can be implemented via: ArcToolbox>Spatial Statistics Tools>Measuring Geographic Distributions>Mean Center


1 2 32 4 73 7 74 7 35 6 2

sum 26 22Centroid/MC 5.2 4.4

n

YY

n

XX

n

i

i

n

i

i 11 ,

0 105

010

5

2,3

7,7

7,3

6,2

4,7

Calculating the centroid of a polygon or the mean center of a set of points.

(same example data as for area of polygon)

i X Y weight wX wY

1 2 3 3,000 6,000 9,0002 4 7 500 2,000 3,5003 7 7 400 2,800 2,8004 7 3 100 700 3005 6 2 300 1,800 600

sum 26 22 4,300 13,300 16,200w MC 3.09 3.77

Calculating the weighted mean center. Note how it is pulled toward the high weight point.

i

n

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w

YwY

w

XwX 11 ,

0 105

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2,3

7,7

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4,7

Median Center:Intersection of a north/south and an east/west line drawn so half of population lives above and half below the e/w line, and half lives to the left and half to the right of the n/s line.Same as “point of minimum aggregate travel” the location that would minimize travel distance if we brought all US residents straight to one location. Mean Center:

Balancing point of a weightless map, if equal weights placed on it at the residence of every person on census day.Note: minimizes squared distances. The point is considerable west of the median center because of the impact of “squared distance” to “distant” populations on west coast

Source: US Statistical Abstract 2003

For a fascinating discussion of the effect of population projection see: E. Aboufadel & D. Austin, A new method for calculating the mean center of population center of the US Professional Geographer, February 2006, pp. 65-69


Standard Distance Deviation single unit measure of the spread or dispersion of a distribution.

• Is the spatial equivalent of standard deviation for a single variable• Equivalent to the standard deviation of the distance of each point from the mean

center• Given by:

which by Pythagorasreduces to:

---the square root of the average squared distance---essentially the average distance of points from the center We can also weight each point and calculate weighted standard distance (analogous

to weighted mean center.)

N

YYXXn

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icici

1 1

22 )()(

N

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iiC 1

2


Standard Distance Deviation Example

i X Y (X - Xc)2 (Y - Yc)2

1 2 3 10.2 2.02 4 7 1.4 6.83 7 7 3.2 6.84 7 3 3.2 2.05 6 2 0.6 5.8

sum 26 22 18.8 23.2Centroid 5.2 4.4

sum 42.00divide N 8.40sq rt 2.90

N

YYXXsdd

n

i

n

icici

1 1

22 )()(

0 105

010

5

2,3

7,7

7,3

6,2

4,7

i X Y (X - Xc)2 (Y - Yc)2

1 2 3 10.2 2.02 4 7 1.4 6.83 7 7 3.2 6.84 7 3 3.2 2.05 6 2 0.6 5.8

sum 26 22 18.8 23.2Centroid 5.2 4.4

sum of sums 42divide N 8.4sq rt 2.90

Circle with radii=SDD=2.9

Standard Deviational Ellipse: concept• Standard distance deviation is a good single measure of the dispersion of

the incidents around the mean center, but it does not capture any directional bias– doesn’t capture the shape of the distribution.

• The standard deviation ellipse gives dispersion in two dimensions• Defined by 3 parameters

– Angle of rotation– Dispersion along major axis– Dispersion along minor axisThe major axis defines the

direction of maximum spreadof the distribution

The minor axis is perpendicular to itand defines the minimum spread


Standard Deviational Ellipse: example

For formulae for its calculation, see Lee and Wong Statistical Analysis with ArcView GIS pp. 48-49 (1st ed.), pp 203-205 (2nd ed.)

There appears to be no major difference between the location of the software and telecommunications industry in North Texas.

Spatial Operations: buffer zones• region within ‘x’ distance units• buffer any object: point, line or

polygon• use multiple buffers at progressively

greater distances to show gradation• may define a ‘friction’ or ‘cost’ layer

so that spread is not linear with distance

• Implement in Arcview 3.2 with Theme/Create buffers

in ArcGIS 8 with ArcToolbox>Analysis Tools>Buffer

Examples• 200 foot buffer around property

where zoning change requested• 100 ft buffer from stream center

line limiting development• 3 mile zone beyond city

boundary showing ETJ (extra territorial jurisdiction)

• use to define (or exclude) areas as options (e.g for retail site) or for further analysis

• in conjunction with ‘friction layer’, simulate spread of fire

polygon buffer

linebuffer

point buffers

Note: only one layer is involved, but the buffer can be output as a new layer

Criteria may be:– formal (based on in situ characteristics)

e.g. city neighborhoods– functional (based on flows or links):

e.g. commuting zones

Groupings may be:– contiguous– non-contiguous

Boundaries for original polygons:– may be preserved– may be removed (called dissolving)

Examples:• elementary school zones to high school attendance zones (functional

districting)• election precincts (or city blocks) into legislative districts (formal

districting)• creating police precincts (funct. reg.)• creating city neighborhood map (form. reg.)• grouping census tracts into market segments--yuppies, nerds, etc (class.)• creating soils or zoning map (class)

Implement in ArcView 9 thru ArcToolbox>Generalization>Dissolve

Spatial Operations: spatial aggregation

• districting/redistricting– grouping contiguous polygons

into districts– original polygons preserved

• Regionalization (or dissolving)– grouping polygons into

contiguous regions– original polygon boundaries

dissolved

• classification– grouping polygons into non-

contiguous regions– original boundaries usually

dissolved– usually ‘formal’ groupings

Grouping/combining polygons—is applied to one polygon layer only.


Districting: elementary school attendance zones grouped to form junior high zones.

Regionalization: census tracts grouped into neighborhoods

Classification: cities categorized as central city or suburbssoils classified as igneous, sedimentary, metamorphic


Spatial Operations: Spatial Matching: Spatial Joins and Overlays

• combine two (or more) layers to:– select features in one layer, &/or – create a new layer

• used to integrate data having different spatial properties (point v. polygon), or different boundaries (e.g. zip codes and census tracts)

• can overlay polygons on:– points (point in polygon)– lines (line on polygon)– other polygons (polygon on polygon)– many different Boolean logic

combinations possible» Union (A or B)» Intersection (A and B)» A and not B ; not (A and B)

• can overlay points on:– Points, which finds & calculates distance

to nearest point in other theme– Lines, which calculates distance to nearest

line

Examples• assign environmental samples

(points) to census tracts to estimate exposure per capita (point in polygon)

• identify tracts traversed by freeway for study of neighborhood blight (polygon on lines)

• integrate census data by block with sales data by zip code (polygon on polygon)

• Clip US roads coverage to just cover Texas (polygon on line)

• Join capital city layer to all city layer to calculate distance to nearest state capital(point on point)


•ERASE - erases the input coverage features that overlap with the erase coverage polygons.

•CLIP - extracts those features from an input coverage that overlap with a clip coverage. This is the most frequently used polygon overlay command to extract a portion of a coverage to create a new coverage.

Example: Spatial Matching: Clipping and Erasing

(sometimes referred to as spatial extraction)

Note: the definition of Union in GIS is a little different from that in mathematical set theory. In set theory, the union contains everything that belongs to any input set, but original set membership is lost. In a GIS union, all original set memberships are explicitly retained. In set theory terms, the outcome of the above would simply be:

Example: Spatial Matching via Polygon-on-Polygon Overlay: Union

DrainageBasins

The two layers (land use & drainage basins) do not have common boundaries. GIS creates combined layer with all possible combinations, permitting calculation of land use by drainage basin.

a. b. c.

aGaA bA

bGcAcG

Land Use

A.G.

Atlantic

Gulf

Combined layer

GIS Union Set Theory Union

1 2 3

Another example

Available in three places• via Selection/Select by Location

– this selects features of one layer(s) which relate in some specified spatial manner to the features in another layer

– if desired, selected features may be saved later to a new theme via Data/Export Data– Individual features are not themselves modified

• via Spatial Join (right click layer in T of C, select Join/Joins and Relates, then click down arrow in first line of Join Data window---see Joining Data in Help for details)

– Use for: points in polygonlines in polygonpoints on lines (to calculate distance to nearest line)points on points (to calculate distance to “nearest neighbor” point)

– operate on tables and normally creates a new table with additional variables, but again does not modify spatial features themselves

• via ArcToolbox – Generally these tools modify geographic feature, thus they create a new layer (e.g. shape file)– Tools are organized into multiple categories

ArcToolbox Examples• Dissolve features based on an attribute

– Combine contiguous polygons and remove common border– ArcToolbox>Generalization>Dissolve

• Clip one layer based on another– ArcToolbox>Analysis Tools>Extract>Clip– Use one theme to limit features in another theme

(e.g. limit a Texas road theme to Dallas county only)• Intersect two layers (extent limited to common area)

– ArcToolbox>Analysis Tools>Overlay>Intersect– Use for polygon on polygon overlay

• Union two layers (covers full extent of both layers) – ArcToolbox>Analysis Tools>Overlay>Intersect– Use for polygon on polygon overlay

Implementing Spatial Matching in ArcGIS 9


Spatial Operations:neighborhood analysis/spatial filtering

• spatial convolution or filter– applied to one raster layer– value of each cell replaced by some

function of the values of itself and the cells (or polygons) surrounding it

– can use ‘neighborhood’ or ‘window’ of any size

» 3x3 cells (8-connected)» 5x5, 7x7, etc.

– differentially weight the cells to produce different effects

– kernel for 3x3 mean filter:1/9 1/9 1/91/9 1/9 1/91/9 1/9 1/9

• low frequency ( low pass) filter:

mean filter

– cell replaced by the mean for neighborhood

– equivalent to weighting (mutiplying) each cell by 1/9 = .11 (in 3x3 case)

– smooths the data – use larger window for greater smoothing

median filter– use median (middle value) instead of

mean– smoothing, especially if data has

extreme value outliersweights must sum to 1.0


Spatial Operations:spatial filtering -- high pass filter

high frequency (high pass) filter

negative weight filter

– exagerates rather than smooths local detail

– used for edge detection

standard deviation filter (texture transform)

– calculate standard deviation of neighborhood raster values

– high SD=high texture/variability

– low SD=low texture/variability

– again used for edge detection

– neighorhoods spanning border have large SD ‘cos of variability

2 51(5)(9)+5(5)(-1)+3(2)(-1) = 14

1(2)(9)+5(2)(-1)+3(5)(-1) = -7

1(5)(9)+8(5)(-1) = 5

1(2)(9)+8(2)(-1) = 2

cell values (vi ) on

each side of edge

–kernel for example (wi)

-1 -1 -1

-1 9 -1

-1 -1 -1

filtered values forhighlighted pixel

fi.vi

.wi


Spatial Operations:raster–based modelling

• Relating multiple rasters• Processes may be:

– Local: one cell only– Neighborhood: cells relating to

each other in a defined manner– Zonal: cells in a given section– Global: all cells

• ArcGIS implementation:– All raster analyses require either

the Spatial Analyst or 3-D Analyst extensions

– Base ArcView can do no more than display an image (raster) data set

• Suitability modeling

• Diffusion Modeling

• Connectivity Modeling

soil slope forsale

Siteoptions

Incidence matrix

Probability mask

System attime t+1

Connectivitymatrix

InitialState

ResultantState

1

0

1 1 1

00 01

3

1

2

2

1

0

0


Attribute Operations: record selection or extraction--features selected on the map are identified in the table (and visa versa)

Select by Attribute (tabular)• Independent selection by clicking table rows:

– Open Attribute Table & click on grey selection box at start of row (hold ctrl for multiple rows)

• Create SQL query – use Selection/Select by Attribute

• use table Relates /Joins to select specific dataSelect by Graphic • Manually, one point at a time

– use Select Features tool • within a rectangle or an irregular polygon

– use Selection/Select by Graphic • within a radius (circle) around a point or points

– use Selection/Select by Location (are wthin distance)

Select by Location• By using another layer

– Use Selection/Select by Location

(same as Spatial Matching discussed previously)Hot Link• Click on map to ‘hot link’ to pictures, graphs, or

other maps

Outputs may be:• Simultaneously highlighted

records in table, and features on map

• New tables and/or map layers

Examples• Use SQL query to select all zip

codes with median incomes above $50,000 (tabular)

• identify zip codes within 5 mile radius of several potential store sites and sum household income (graphic)

• show houses for sale on map, and click to obtain picture and additional data on a selected house (hot link)


Attribute Operations:statistical analysis on one or more columns in

table• univariate (one variable or column)

– central tendency: mean, median, mode– dispersion: standard deviation, min, max– To obtain these statistics in ArcGIS:

» Right click in T of C and select Open attribute table » Right click on column heading and select Statistics

• bivariate (relating two variables or columns)– interval and nominal scale variables: sum or mean by category

» average crop yield by silt-sand-clay soil types» To implement in ArcGIS, proceed as above but use Summarize

– two interval scale variables: correlation coefficients» income by education» ArcScripts are available for this on ESRI web site (or use Excel!)

• multivariate (more than two variables)– usually requires external statistical package such as SAS, SPSS, STATA or S-

PLUS


• establishing/modifying number of classes and/or their boundaries for continuous variable. Options for ArcGIS

– natural breaks (default)(finds inherent inherent groups via Jenks optimization which minimizes the variances within each of the classes).

– quantile (classes contain equal number of records--or equal area under the frequency distribution)

– equal interval (user selects # of classes)(equal width classes on variable)

– Defined interval (user selects width of classes)(equal width classes on variable)

– standard deviation(categories based on 1,2, etc, SDsabove/below mean)

– Manual (user defined)» whole numbers (e.g. 2,000)» meaningful to phenomena (e.g zero, 32o)

• aggregating categories on a nominal (or ordinal) variable– pine and fir into evergreen

No change in number of records (observations).

Attribute Operations: variable recoding

0-1 1-2 2

34%14% 34% 14%

.68-.68

23%23% 25%

0

(assumes a Normal distribution)

25%

Standard Deviation

Implement in ArcGIS via:Right click in T of C, select Properties, then Symbology tab

Equal area %

Equal interval %

Equal interval score

Equal area score


Attribute Operations: record aggregation

• combining two or more records into one, based on common values on a key variable

• the attribute equivalent of regionalization or classification

• equivalent of PROC SUMMARY in SAS

• interval scale variables can be aggregated using mean, sum, max, min, standard deviation, etc. as appropriate

• ordinal and nominal require special consideration

• example: aggregate county data to states, or county to CMSA

Record count decreases (e.g. from 12 to 2)

Fips PMSA_90 PMSA_93 Pop90 Pop95_est Pop90-95% MedInc89 Suburb Name48085 1920 1920 264036 346232 5.93 46020 1 Collin48113 1920 1920 1852810 1959281 1.09 31605 0 Dallas48121 1920 1920 273525 334070 4.22 36914 1 Denton48139 1920 1920 85167 94223 2.03 30553 1 Ellis48213 1920 58543 64293 1.87 20747 1 Henderson48231 1920 64343 66972 0.78 25317 1 Hunt48257 1920 1920 52220 60114 2.88 27280 1 Kaufman48397 1920 1920 25604 32725 5.30 42417 1 Rockwall48221 2800 28981 33384 2.89 31627 1 Hood 48251 2800 2800 97165 106181 1.77 30612 1 Johnson48367 2800 2800 64785 73794 2.65 30592 1 Parker48439 2800 2800 1170103 1278606 1.77 32335 0 Tarrant

Source: US Bureau of the CensusMedInc=Median Household Income. Pop90 as of April 1. Pop95 as of July 1.

Fips PMSA_90 PMSA_93 Pop90 Pop95_est Pop90-95% MedInc89 Suburb Name1920 2676248 2957910 2.00 32607 7 Dallas2800 1361034 1491965 1.83 31292 3 Fort Worth

sum re-calc.average

ofmedians!

countsum

Type of processing:


Attribute Operations: Joining and Relating Tablesassociating spatial layer to non-spatial table

Join: one to one, or one to many, relationship appends attributesAssociate table of country capitals with country layer: only one capital for

each country (one to one)

Associate country layer with type of government: one gov. type assigned to many countries--but each country has only one gov. type (one to many)

Country Code Country29 France68 Saudi Arabia

106 Chad248 Spain199 Venezuela

9 UK96 Philippines

Layer Attribute Table

Country Code Capital199 Caracas96 Manila68 Riyadh29 Paris

106 N'Djamena9 London

248 MadridNonSpatial Table

Country Code Country Capital9 UK London29 France Paris68 Saudi Arabia Riyadh96 Philippines Manila

106 Chad N'Djamena199 Venezuela Caracas248 Spain Madrid

Layer Attribute Table after Join

Gov. Code Country20 France30 Vietnam15 UK20 Argentina10 Saidi Arabia15 Sweden45 Portugal


Gov. Code Type10 Absolute Monarchy15 Const. Monarchy20 Republic30 Communist State45 Parliamentary Democracy

NonSpatial Table

Gov. Code Country Type20 France Republic30 Vietnam Communist State15 UK Const. Monarchy20 Argentina Republic10 Saudi Arabia Absolute Monarchy15 Sweden Const. Monarchy45 Portugal Parliamentary Democracy

Layer Attribute Table after Join


NAME FIPS CODE POP2000Alabama 01 AL 4,447,100Alaska 02 AK 626,932…..Georgia 13 GA 8,186,453Hawaii 15 HI 1,211,537Idaho 16 ID 1,293,953…..Wisconsin 55 WI 5,363,675Wyoming 56 WY 493,782

51 states Total 282,421,906

Single most common error in GIS Analysis--intending a one to one join of attribute to spatial table--getting a one to many join of attributes to spatial table

Spatial

FID SHAPE NAME FIPS CODE POP20000 Polygon Alabama 01 AL 4,447,1001 Polygon Alaska 02 AK 626,932

…..11 Polygon Georgia 13 GA 8,186,45312 Polygon Hawaii-Hawaii 15 HI 1,211,53713 Polygon Hawaii-Maui 15 HI 1,211,53714 Polygon Hawaii-Oahu 15 HI 1,211,53715 Polygon Hawaii-Kauai 15 HI 1,211,53716 Polygon Idaho 16 ID 1,293,953

…..53 Polygon Wisconsin 55 WI 5,363,67554 Polygon Wyoming 56 WY 493,782

Total 286,056,517

After joining attribute to spatial data


Attribute Operations: Joining and Relating Tablesassociating spatial layer to non-spatial table

(contd.)

Relate: many to one relationship, attributes not appendedAssociate country layer with its multiple cities (many to one)

Note: if we flip these tables we can do a join since there is only one country for each city (one to many)

For both Joins and Relates:• Association exists only in the map document• Underlying files not changed unless export data

Country Code Country29 France68 Saudi Arabia

106 Chad248 Spain199 Venezuela

9 UK96 Philippines


Country Code City129 Mombasa129 Nairobi29 Paris29 Lyon29 Marseille60 Katmandu

248 Madrid248 Barcelona248 Valencia

NonSpatial Table

If joined Paris to France, for example, we lose Lyon and Marseille, therefore use relate


Analysis Options: Advanced & Specialized(Table of Contents)

Advanced• Proximity/point pattern analysis

– nearest neighbor layer– distance matrix layer

• surface analysis– cross section creation– visibility/viewshed

• network analysis– routing

» shortest path (2 points)» travelling salesman (n points)

– time districting– allocation

• Convex Hull

• Thiessen Polygon creation

Specialized• Remote Sensing image

processing and classification

• raster modeling• 3-D surface modeling• spatial statistics/statistical

modeling• functionally specialized

– transportation modeling– land use modeling– hydrological modeling– etc.


Advanced Applications: Proximity Analysis Nearest Neighbor• location (distance) relative to nearest neighbor

( points or polygon centroids)• location (distance) relative to nearest objects of

selected other types (e.g. to line, or point in another layer, or polygon boundary)

Requires only one output column – altho generalizable to kth nearest neighbor

Full matrix• measure location of each object relative

to every other object– requires output matrix with as many

columns as rows in input table

Point Pattern Analysis is pattern?

Random Clustered Dispersed

Requires the application of Spatial Statistics such as•Nearest neighbor statistic•Moran’s I which are based on proximity of points to each other ArcToolbox>Spatial Statistics Tools


Advanced Applications:Network Analysis

Routing

• shortest path between two points– direction instructions (locating

hotel from airport)

• travelling salesman: shortest path connecting n points– bus routing, delivery drivers

Network-based Districting• expand from site along network until

criteria (time, distance, cost, object count) is reached; then assign area to district

– creating market areas, attendance zones, etc– essentially network-based buffering

Network-based Allocation• assign locations to the nearest center

based upon travel thru network– assign customers to pizza delivery outlets

• draw boundaries (lines of equidistance between 2 centers) based on the above

– Network-based market area delimitation– Essentially, network-based polygon

tesselation

In all cases, ‘distance’ may be measured in miles, time, cost or other ‘friction’ (e.g pipe diameter for water, sewage, etc.). Arc or node attributes (e.g one-way streets, no left turn) may also be critical.


Advanced applications:Surface Analysis

Slope Transform• fit a plane to the 3 by 3

neighborhood around every cell, or use a TIN

• output layer is the slope (first derivative) of the plane for each cell

Aspect Transform• direction slope faces: (E-W oriented

ridge has slopes with northern and southern aspects)

• aspect normally classified into eight 45 degree categories

• calculate as horizontal component of the vector perpendicular to the surface

Cross-section Drawings and Volumes• elevation (or slope) values along a line• Volume & cut-and-fill calculation• Cross-section easy to produce for raster,

more difficult for vector especially if uses contours lines

Viewshed/Visibility• terrain visible from a specific point• applications

– visual impact of new construction– select scenic overlooks– Military

• Contouring– Lines joining points of equal (vertical)

value– From raster, massed-points or breakline data


Advanced Applications: Convex Hull

• Formally: the smallest convex polygon (no concave angles) able to contain a set of points

• Informally: a rubber band wrapped around a set of points

• Just as a centroid is a point representation for a polygon, the convex hull is the polygon representation for a set of points

• Go to the following web site for a neat application showing how convex hull changes as you move points around

–http://www.cs.princeton.edu/~ah/alg_anim/version1/ConvexHull.html

No!


Advanced Applications:Thiessen (Dirichlet, Voronoi) Polgons

and Delaunay Triangles• polygons generated from a point layer such that any

location within a polygon is closer to the enclosed point than to a point within any other polygon

• they divide the space between the points as ‘evenly’ as possible

• used for market area delimitation, rain gauge area assignment, contouring via Delaunay triangles (DTs), etc.

• elevation, slope and aspect of triangle calculated from heights of its three corners

• DTs are as near equiangular as possible and longest side is as short as possible, thus minimizes distances for interpolation

A

Thiessen neighbors of point A share a common boundary. Delauney triangles are formed by

joining point to its Thiessen neighbors.

A

Thiessen Polygons (or proximal regions or proximity polygons)

Delaunay Triangles


Specialized ApplicationsRemote Sensing/Digital Image

Processing• reflectance value (usually 8 bit; 256

values) collected for each bands (wavelength area) in the electro-magnetic spectrum

– 1 band for grey scale (Black & white)– 3 for color – up to 200 or so for ‘hyperspectral’– permits creation of image

• ‘spectral signature’: set of reflectance values/ranges over available bands typifying a specific phenomena

– provides basis for identification of phenomena

Location Science/Network Modeling• Network based models for optimum

location decisions for (e.g.) – police beats– School attendance zones– Bus routes– Hazardous material routing– Fire station location

Raster Modeling: 2-D• use of direction and friction surfaces to

develop models for:– spread of pollution– dispersion of forest fires

Surface Modeling: 3-D– flood potential– ground water/reservoir studies– Viewshed/visibility analysis

Spatial Statistics/Econometrics• analyses on spatial data which explicitly

incorporates relative location or proximity property of observations

Global (applies to entire study area)– spatial autocorrelation– Regressions adjusted for spatial

autocorrelationLocal (separately calculated for local areas)– LISA (local indicators of spatial

autocorrelation)– Geographically weighted regression

We offer one or more courses on each!


Implementation of Advanced and Specialized Applications in ArcGIS 8/9

Extensions support many of the Advanced and some Specialized Applications• Spatial Analyst extension provides 2-D modeling of GRID (raster) data (AV 3.2 and 8/9) • 3-D Analyst extension provides 3-D modeling (AV 3.2 and 8/9)• Geostatistical Analyst extension provides interpolation (ArcGis 8/9 only)• Network Analyst extension (3.2 only) and ArcLogistics Route (standalone) for routing and

network analysis • Image Analyst extension for remote sensing applications in AV 3.2

– Leica Image Analysis and Stereo Analyst for ArcGIS 8 (9 version not yet released-Fall ’04)

• Spatial Statistics Tools in ArcToolbox provide spatial statistics (centroid, etc..)ArcScripts support other Advanced Applications and Specialized Applications • ArcScripts (in Visual Basic, C++, etc.) are used to customize ArcGIS 8

– A variety of scripts available at http://support.esri.com/ >downloads – Note: ArcScripts written in Avenue work only in ArcView 3 and will not work in ArcGIS 8/9 – Many functions previously requiring Avenue scripts for AV 3.2 are built into ArcGIS 8/9

Specialized Software Packages• Remote Sensing packages such as Leica GeoSystems Imagine (formerly ERDAS Imagine)• For links to some of these packages go to: http://www.utdallas.edu/~briggs/other_gis.html


Appendix

Implementing Spatial Analysis in

ArcView 3.2/3.3


Implementing Spatial Measurement in ArcView 3.2• Unlike in ArcGIS 8.1 where spatial measurements are provided automatically, in

AV 3.2 spatial measurement often has to be implemented using Avenue code:– in functional expressions– in scripts

• Using functional expression for areas and lengths:– Use Edit/Add field to add a new variable to atttributes of…table called area (or similar)– Use Field/calculate and make this variable equal to: [Shape].ReturnArea– Calculation is based on map units irrespective of defined distance units.– If map units are feet, to obtain square miles use: [Shape].ReturnArea/5280/5280

– If file is a polyline file (arcs), for length of arcs use: [Shape].ReturnLength • Using a Script for areas, perimeters and lengths

In Project window, select Script and click new button to open script windowUse Script/load text file to load code from an existing text file

e.g. arcview\samples\scripts\calcapl.ave will calculate areas, perimeters, lengthsClick the “check mark” icon to compile the code.Open a View and be sure the theme you want processed is active.Click on script window then click the Runner icon to run script.

variables measuring area and perimeter will be added to theme table


Available in two places (plus additional user extensions such as districting) • via Theme/select by theme

– this selects features of the active theme which relate in some specified spatial manner to another theme– if desired, selected features may be saved later to a new theme via Theme/convert to shape file

• via Geoprocessing Wizard Extension (use File/Extensions to load)– this creates a new theme (shape file) & combines attribute tables from 2 or more input themes– Six options available for different types of matching

Options in Geoprocessing Wizard (use View/Geoprocessing Wizard to activate)• Dissolve features based on an attribute

– Use for spatial aggregation/dissolving• Merge themes together

– Use for edge matching• Clip one theme based on another

– Use one theme to limit features in another theme(e.g. limit a Texas road theme to Dallas county only)

• Intersect two themes– Use for polygon on polygon overlay

• Union two themes– Use for polygon on polygon overlay

• Assign data by location (Spatial Join)– Use for: points in polygon

lines in polygonpoints on lines (to calculate distance to nearest line)points on points (to calculate distance to “nearest neighbor” point)

•Scripts and extensions can provide additional capabilities•Download from ArcScripts at ESRIhttp://gis.esri.com/arcscripts/scripts.cfm•Place extensions (.avx) in your folder Arcview/ext32•The extension district.avx is good for doing spatial aggregation or “districting”

Implementing Spatial Matching in ArcView 3.2


Using Extensions and Scripts in ArcView 3.2• Obtain copy of script or extension

– Write yourself with Avenue language– Supplied with ArcView in folder: arcview/samples/scripts or arcview/samples/ext

» Go to ArcView Help/Contents/Sample Scripts and Extensions for documentation

– Buy from ESRI and other companies– Supplied free by ESRI or users and available on ESRI web site at: http://arcsripts.esri.com/ Select Avenue language

» or go to www.esri.com and click Support» Be sure to print or download documentation/description

• To load and use an extension– Place .avx file in arcview/ext32 folder– Open ArcView, choose File/extensions, place tick next to name, click OK

• To load and use a scriptIn Project window, select Script and click new button to open script windowUse Script/load text file to load code from existing text file containing avenue code (.ave)

e.g. \av_gis30\arcview\samples\scripts\calcapl.ave will calculate areas, perimeters, lengths

Click the “check mark” icon to compile the code.Take steps within ArcView as appropriate for specific script

e.g. Open a View and be sure the theme you want processed is active.

Click on script window then click the “Runner" icon to run script.e.g. variables measuring area and perimeter will be added to theme table


Some Example Avenue Scripts for ArcView 3

• Avenue scripts and extensions for AV 3.2 can be downloaded from ESRI Web site to do many basic, advanced and specialized applications not available in standard products. Some examples are:– Addxycoo.ave: adds X,Y coordinates of points (e.g of geocoded

addresses), or of centroid for polygons, to attributes of … file

– Polycen.ave: creates point theme containing polygon centroids

– Dwizard.zip: various districting applications» Use avdist31b which is an update

– Line.zip: enhanced buffering of lines

– Nearestneighbor.zip: nearest neighbor analysis

For more scripts, go to: http://arcsripts.esri.com/ Select Avenue language

1 8/19/2014 ron briggs, utdallas gisc 6381 gis fundamentals analysis and modeling in gis

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