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Analysis and Modeling in GIS. GIS and the Levels of Science. Description: 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.

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gis and the levels of science
GIS and the Levels of Science

Description:

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 descriptiveinput 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
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 corridor

Determine the proportion of a region covered by igneous extrusions

Do 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
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 Operations

Vector

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

Description and Basic Analysis(Table of Contents)
spatial operations spatial measurement
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

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 B

P = 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

= 1.0 for circle

= 1.13 for square

Large for complex shape

area x 3.54

Spatial operations: Spatial Measurement

ArcGIS geodatabases contain automatic variables:

shape.length: line length or

polygon perimeter

shape.area: polygon area

Automatically 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

spatial operations spatial measurement7
Spatial operations: Spatial Measurement

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

10

Area=(2 x 4)/2=4

Area=(3 x 4)=12

4,7

10

7,7

5

7,3

2,3

5

0

0

0

0

10

10

10

10

5

5

5

5

Area=(5 x 1)/2=2.5

6,2

0

0

5

5

Spatial Measurement: Calculating the Area of a Polygon

-

A

B

C

=

-

=

10

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:

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

Its implementation in Excel is shown below.

0

spatial operations centrographic statistics
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 distribution

Measures of Centrality (equivalent to mean)

    • Mean Center and Centroid

Measures 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

centroid outside

polygon

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

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

slide11

4,7

7,7

10

10

4,7

7,7

7,3

2,3

5

5

6,2

0

0

10

10

5

5

7,3

2,3

6,2

0

0

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

(same example data as for area of polygon)

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

slide12

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

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

Source: US Statistical Abstract 2003

standard distance deviation single unit measure of the spread or dispersion of a distribution
Standard Distance Deviationsingle 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.)

standard distance deviation example

4,7

7,7

10

7,3

2,3

5

6,2

0

10

5

0

Standard Distance Deviation Example

Circle with radii=SDD=2.9

standard deviational ellipse concept
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 capturethe 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 axis

The 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
Standard Deviational Ellipse: example

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

For formulae for its calculation, see

Lee and Wong Statistical Analysis with ArcView GIS pp. 48-49 (1st ed.), pp 203-205 (2nd ed.)

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

Spatial Operations: buffer zones

polygon buffer

point

buffers

line

buffer

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

spatial operations spatial aggregation
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)

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.

Spatial Operations: spatial aggregation

Implement in ArcView 9 thru ArcToolbox>Generalization>Dissolve

slide19

Districting: elementary school attendance zones grouped to form

junior high zones.

Regionalization: census tracts grouped into neighborhoods

Classification: cities categorized as central city or suburbs

soils 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)

Spatial Operations:Spatial Matching:Spatial Joins and Overlays
erase erases the input coverage features that overlap with the erase coverage polygons

Example: Spatial Matching: Clipping and Erasing

(sometimes referred to as spatial extraction)

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 via polygon on polygon overlay union

c.

Land Use

a.

b.

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.

Drainage

Basins

Atlantic

A.

G.

cA

Gulf

bG

cG

bA

aA

aG

GIS Union

Set Theory Union

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

Combined layer

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:

Another example

1

2

3

implementing spatial matching in arcgis 9
Implementing Spatial Matching in ArcGIS 9

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 polygon

lines in polygon

points 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
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 1/9

1/9 1/9 1/9

1/9 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)

smoothsthe data

use larger window for greater smoothing

median filter

use median (middle value) instead of mean

smoothing, especially if data has extreme value outliers

Spatial Operations:neighborhood analysis/spatial filtering

weights 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

Spatial Operations:spatial filtering -- high pass filter

cell values (vi ) on

each side of edge

filtered values for

highlighted pixel

2

5

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

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

  • kernel for example (wi)
  • -1 -1 -1
  • -1 9 -1
  • -1 -1 -1

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

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

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

Spatial Operations:raster–based modelling

0

0

1

0

Site

options

0

1

for

sale

2

0

soil

slope

1

0

1

1

1

1

3

2

System at

time t+1

Incidence

matrix

Probability

mask

Initial

State

Connectivity

matrix

Resultant

State

slide27
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

useSelection/Select by Attribute

use table Relates /Joins to select specific data

Select 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: record selection or extraction--features selected on the map are identified in the table (and visa versa)
attribute operations statistical analysis on one or more columns in table
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
attribute operations variable recoding

(assumes a Normal distribution)

25%

25%

23%

23%

14%

34%

34%

14%

-2

-1

0

1

2

-.68

.68

0

Attribute Operations: variable recoding
  • 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, SDs

above/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).

Implement in ArcGIS via:

Right click in T of C, select Properties, then Symbology tab

Equal area %

Equal interval %

Standard Deviation

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)

Attribute Operations:record aggregation

re-calc.

sum

sum

count

average

of

medians!

Type of processing:

attribute operations joining and relating tables associating spatial layer to non spatial table
Attribute Operations: Joining and Relating Tablesassociating spatial layer to non-spatial table

Join: one to one, or one to many, relationship appends attributes

Associate 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)

slide32

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

After joining attribute to spatial data

slide33
Attribute Operations: Joining and Relating Tablesassociating spatial layer to non-spatial table(contd.)

Relate: many to one relationship, attributes not appended

Associate 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

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.

Analysis Options: Advanced & Specialized(Table of Contents)
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

RandomClusteredDispersed

Advanced Applications: Proximity Analysis
  • Point Pattern Analysis
    • is pattern?
  • 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
  • Full matrix
  • measure location of each object relative to every other object
    • requires output matrix with as many columns as rows in input table
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

Advanced Applications:Network Analysis

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:Surface Analysis
advanced applications convex hull

No!

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
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

A

Advanced Applications:Thiessen (Dirichlet, Voronoi) Polgonsand Delaunay Triangles

Thiessen Polygons

(or proximal regions

or proximity polygons)

Delaunay Triangles

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

specialized applications
Remote 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 autocorrelation

Local (separately calculated for local areas)

LISA (local indicators of spatial autocorrelation)

Geographically weighted regression

Specialized Applications

We offer one or more courses on each!

implementation of advanced and specialized applications in arcgis 8 9
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 extensionprovides 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

Appendix

Implementing Spatial Analysis in

ArcView 3.2/3.3

implementing spatial measurement in arcview 3 2
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 window

Use Script/load text file to load code from an existing text file

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

Click 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

implementing spatial matching in arcview 3 2
Implementing Spatial Matching in ArcView 3.2

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 shapefile
  • 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 polygon

points 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 ESRI
  • http://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”
using extensions and scripts 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 script

In Project window, select Script and click new button to open script window

Use 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
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

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