spatial analysis cont. density estimation, summary spatial statistics, routing
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Spatial Analysis cont. Density Estimation, Summary Spatial Statistics, Routing. Longley et al., chs. 13,14. Density Estimation. point interpolation to estimate a continuous surface vs. density estimation - surface is estimated from counts within polygons - PowerPoint PPT PresentationTRANSCRIPT
Spatial Analysis cont.Density Estimation, Summary Spatial
Statistics, Routing Longley et al., chs. 13,14
Density Estimation
• point interpolation to estimate a continuous surface
vs.• density estimation - surface is estimated
from counts within polygons– (e.g., population density surface derived from total
population counts in each reporting zone)
Objects to Fields
• map of discrete objects and want to calculate their density– density of population
– density of cases of a disease
– density of roads in an area
• density would form a field• one way of creating a field from a set of
discrete objects
Density Estimation and Potential
• Spatial interpolation is used to fill the gaps in a field
• Density estimation creates a field from discrete objects– the field’s value at any point is an estimate of the density
of discrete objects at that point
– e.g., estimating a map of population density (a field) from a map of individual people (discrete objects)
Density Estimation Using Kernels
• Mathematical function• each point replaced by a “pile of sand” of
constant shape• add the piles to create a surface
Width of Kernel
• Determines smoothness of surface – narrow kernels
produce bumpy surfaces
– wide kernels produce smooth surfaces
Example
• Density estimation and spatial interpolation applied to the same data
• density of ozone measuring stations
vs.• Interpolating surface based on locations
of ozone measuring stations
Using Spatial Analyst
Kernal too small?(radius of 16 km)
Kernel radius of 150 km
What’s the Difference?
Summary Spatial Statistics
Longley et al., chs. 5,14
Descriptive Summaries
• Ways of capturing the properties of data sets in simple summaries
• mean of attributes• mean for spatial coordinates, e.g.,
centroid
Spatial Min, Max, Average
An example of the use of centroids to summarize the changes in point patterns through time. The centroids of four land use classes are shown for London, Ontario, Canada
from 1850 to 1960. Circles show the associated dispersions of sites within each class. Note how the industrial class has moved east, remaining concentrated, while the
commercial class has remained concentrated in the core, and the residential class has dispersed but remained centered on the core. In contrast the institutional class moved
to a center in the northern part of the city.
Spatial Autocorrelation
Arrangements of dark and light colored cells exhibiting negative, zero, and positive spatial autocorrelation.
Tobler’s 1st Law of Geography: everything is related to everything else, but near things are more related than distant things
S. autocorrelation: formal property that measures the degree to which near and distant things are related.
Close in space
Dissimilar in attributes
Attributes independent
of location
Close in space
Similar in attributes
Why Spatial Dependence?
• evaluate the amount of clustering or randomness in a pattern– e.g., of disease rates, accident rates, wealth,
ethnicity
• random: causative factors operate at scales finer than “reporting zones”
• clustered: causative factors operate at scales coarser than “reporting zones”
Moran’s Index• positive when attributes of nearby objects
are more similar than expected• 0 when arrangements are random• negative when attributes of nearby objects
are less similar than expected
I = n wijcij / wij (zi - zavg)2
n = number of objects in sample
i,j - any 2 of the objects
Z = value of attribute for I
cij = similarity of i and j attributes
wij= similarity of i and j locations
Moran’s Indexsimilarity of attributes, similarity of
locationExtreme negative SA Dispersed, - SA
Independent, 0 SA
Spatial Clustering, + SA Extreme positive SA
Crime Mapping• Clustering - neighborhood scale
Geary’s c Ratio
• Like Moran’s Index, use a single value to describe spatial distribution – e.g., of elevations in DEM cells
less than 1 (clustered)
1
greater than 1 (random)• e.g., spatial autocorrelation indicator of
information loss during conversions between DEMs and TINs
Moran’s and Geary’s
Lee and Marion, 1994, Analysis of spatial autocorrelation of USGS 1:250,000 DEMs. GIS/LIS Proceedings.
Fragmentation Statistics
• how fragmented is the pattern of areas and attributes?
• are areas small or large? • how contorted are their boundaries? • what impact does this have on habitat,
species, conservation in general?
Note the increasing fragmentation of the natural habitat as a result of
settlement. Such fragmentation can adversely affect the success
of wildlife populations.
19861975
1992
Fragstats pattern analysis for landscape
ecologyhttp://www.innovativegis.com/products/fragstatsarc/
FRAGSTATS Overview
• derives a comprehensive set of useful landscape metrics
• Public domain code developed by Kevin McGarigal and Barbara Marks under U.S.F.S. funding
• Exists as two separate programs – AML version for ARC/INFO vector data
– C version for raster data
FRAGSTATS Fundamentals
• PATCH… individual parcel (Polygon)
A single homogeneous landscape unitwith consistent vegetation characteristics,e.g. dominant species, avg. tree height,horizontal density ,etc.
A single Mixed Wood polygon(stand)
CLASS… sets of similar parcelsLANDSCAPE… all parcels within an area
FRAGSTATS FundamentalsPATCH… individual parcel (Polygon)
• CLASS… sets of similar parcels
All Mixed Wood polygons(stands)
LANDSCAPE… all parcels within an area
FRAGSTATS FundamentalsPATCH… individual parcel (Polygon)CLASS… sets of similar parcels
• LANDSCAPE… all parcels within an area “of interacting ecosystems”
e.g., all polygons within a given geographic area (landscape mosaic)
FRAGSTATS Output Metrics
• Area Metrics (6),
• Patch Density, Size and Variability Metrics (5),
• Edge Metrics (8),
• Shape Metrics (8),
• Core Area Metrics (15),
• Nearest Neighbor Metrics (6),
• Diversity Metrics (9),
• Contagion and Interspersion Metrics (2)
• …59 individual indices(US Forest Service 1995 Report PNW-GTR-351)
More Spatial Statistics Resources
• Spacestat (www.spacestat.com)
• S-Plus• Alaska USGS freeware
(www.absc.usgs.gov/glba/gistools/)
• Central Server for GIS & Spatial Statistics on the Internet
– www.ai-geostats.org• GEO 441/541 - Spatial Variation in
Ecology & Earth Science
Location-allocation Problems
• Design locations for services, and allocate demand to them, to achieve specified goals
• Goals might include:– minimizing total distance traveled
– minimizing the largest distance traveled by any customer
– maximizing profit
– minimizing a combination of travel distance and facility operating cost
Routing Problems
• Search for optimum routes among several destinations
• The traveling salesman problem– find the shortest tour from an origin, through a
set of destinations, and back to the origin
Routing service technicians for Schindler Elevator. Every day this company’s service crews must visit a different set of locations in Los Angeles. GIS is used to
partition the day’s workload among the crews and trucks (color coding) and to optimize the route to minimize time and cost.
Optimum Paths
• Find the best path across a continuous cost surface– between defined origin and destination
– to minimize total cost
– cost may combine construction, environmental impact, land acquisition, and operating cost
– used to locate highways, power lines, pipelines
– requires a raster representation
Solution of a least-cost path problem. The white line represents the optimum
solution, or path of least total cost, across a friction surface
represented as a raster. The area is dominated by a mountain
range, and cost is determined by elevation and slope. The best
route uses a narrow pass through the range. The blue line results from solving the same
problem using a coarser raster.