using spatial statistics in research: examples from work at ut-dallas faculty research ph.d....
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Using Spatial Statistics in Research:Examples from work at UT-Dallas
Faculty researchPh.D. dissertationsMasters Projects
Former UTD graduates “at work”
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Spatial Autoregressive Model for Population Estimation at the Census Block Level Using LIDAR-derived Building Volume Information
Qiu, Fang*; Sridharan, Harini***; Chun, Yongwan**Cartography and Geographic Information Science,
Volume 37, Number 3, July 2010 , pp. 239-257(19)
*associate professor**assistant professor***Ph.D. candidateUniversity of Texas at Dallas
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Objective• Estimate population in small geographic areas (city block) using remote sensing
data– Cheaper than carrying out a census– Census may not provide data for small areas
Legend
Population1 - 50
51 - 125
126 - 200
201 - 400
>400
500 m
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Previous Work (literature review)• Previous work used remote sensing image
analysis to measure density of roads or area of residential land use– Population then estimated using these data
• Data is only 1 or 2 dimensional– does not measure multi-story housing units– Would not work in China!
• Use LIDAR data to measure building volume
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LiDAR• Light Detection And Ranging (LiDAR) technology• Collects elevation data using a laser scanner
– Laser beam bounces (reflects) back from ground, top of buildings, top or side of trees, etc.
• Produces point cloud of 3-D information – x,y, z: longitude, latitude, elevation
• Very detailed and accurate– Points every few cms if desired
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Data• Obtain building footprints and
their area from analysis of digital ortho images
• Buffer 1m around footprint• Height of building is difference
between median Lidar elevation within footprint (top of building) and median elevation within buffer (ground around building)
• Area x height = volume
Footprint(top of building)
Buffer(ground)
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Model • P=a*Ab allometric growth model used in previous
research
– Population is an increasing function of area (A)
• P=α*Vβ modified allometric growth used in this research– Population is an increasing function of volume (V)
• Log(P) = Log(α)+βLog(V) – Take log of both sides to linearize the equation – use linear regression to estimate the coefficients
Area
Population
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Models R2/
PseudoR2
AIC RMSE Adj
RMSE
OLS
Building volume based 0.844 131.04
28.415 0.4023
Building area based 0.812 139.41
53.581 0.7268
Land use area based 0.638 207.88
53.622 0.4381
Road length based 0.619 185.48
244.50 0.909
SPATIAL MODELS
Building volume based 0.850 128.84
28.173 0.288
Building area based 0.824 138.96 35.072 0.484
Land use area based 0.674 189.61 53.884 0.44
Road length based 0.72 178.55 74.770 0.546
Results
• Volume always better than area or road• Spatial always better than OLS
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Diffusion of WNV across the USDiffusion of WNV across the US
Case study:Case study:A Spatial Analysis of West Nile VirusA Spatial Analysis of West Nile Virus
Daniel A. GriffithAshbel Smith Professor
http://www.ij-healthgeographics.com/articles/browse.aspA comparison of six analytical disease mapping techniques as applied to West Nile Virus in the coterminous United States, International Journal of Health Geographics 2005, 4:18.
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Geographic distribution of West Nile virus (WNV) reported cases
in 2002. Black denotes states with, and white denotes states
without reported cases. % WNV % WNV deaths in deaths in
20032003
% WNV % WNV deaths in deaths in
20042004
2002
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What are the issues/problems?What are the issues/problems?• Predicting where it will spread/occur.• Calculating the correct margin of error for
predicting its occurrence when nearby values are similar (i.e., related).
Why do they need to be resolved?Why do they need to be resolved?• People are dying.How are these issues being addressed?How are these issues being addressed?• Specifying correct spatial statistical models.
Challenges of spatial statistics in analyzing WNV
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Scatterplots of observed versus predicted values
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Surprising spatial filter result: a jump to California
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A Predictive Terrestrial Clutter Model for Ground-to-Ground Automated Target Detection
ApplicationsBy
Gene A. FeighnyPh.D. dissertation, UT-Dallas 2010
Adviser: Dr. Denis Dean(currently Senior Research Engineer, E-Systems Inc.)
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Problem Statement and Objective
• Automated target detection (ATD) algorithms important for both military and civilian use– Identify an “object of interest”:
• tank• plane wreck• “suspicious” package or person
• How do we separate the “object” from the “background clutter”?
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• Clutter has consistent characteristics– Identify those characteristics
• Object will have different characteristics
– It will “stand out”
• Therefore we need to identify the characteristics of clutter
These two scenes obviously have different clutter characteristics
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• What are some of the characteristics of clutter?– degree of spatial clustering at various distances.
• How do we measure this?– Ripley’s K function
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URBAN FOREST INVENTORY USING AIRBORNE LIDAR DATA
AND HYPERSPECTRAL IMAGERY
by Caiyun Zhang
Ph.D. dissertation, UT-Dallas 2010Adviser: Dr Fang Qiu
(Currently, Assistant Professor, Florida Atlantic University)
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Research Objectives1. Develop a relatively simple and robust algorithm to isolate individual
trees using LiDAR vector point cloud data.2. Estimate single tree metrics such as tree heights, tree distributions, stem
density, crown diameters, crown depths, and base heights, from original LiDAR vector data.
3. Develop a neural network based approach to identifying tree species at the individual tree level using the detailed spectral information derived from high spatial resolution hyperspectral images.
4. Produce urban forest 3-D scenes by constructing 3-D tree visualization models using the LiDAR derived information.
5. Map urban forests at the individual tree level using state-of-the-art
geographic information system (GIS) techniques.Point pattern analysis was one of the many techniques
used to meet these objectives.
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Lidar produces a 3-D “point cloud”Various cluster analysis techniques are used to identify different objects
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Turtle Creek, Dallas: Lidar data (laser derived elevations) identifies trees
• Ground Points
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Turtle Creek, Dallas: Hyperspectral data (2151 bands) identifies species
• Ground Points
Accuracy doubled from existing methods: --60%-70% versus 30%-40%
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--one research question to explore is whether or not tree species cluster--in urban forests: No (for U.S.) (they are planted by people)--in natural forests: YES
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Real trees in 2-D image
3-D Forest model based on cluster analysis of Lidar point cloud.--each tree is identified--modeled independently based on height crown depth crown diameter in 4 directions
height
Crown depth
Crown diameter
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Point Cloud Segmentation-based Filtering and Object-based Feature Extraction from Airborne
LiDAR Data
Jie ChangPh.D. Program in Geospatial Sciences
University of Texas at Dallas
May 3, 2010
Proposal for DissertationProposal for Dissertation
Supervising Committee:Supervising Committee:Dr. Ronald BriggsDr. Ronald BriggsDr. Yongwan ChunDr. Yongwan ChunDr. Denis DeanDr. Denis DeanDr. Fang Qiu (Chair)Dr. Fang Qiu (Chair)
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LiDAR Characteristics• LIDAR
– 3D remote sensing– Direct 3D position measurements– Very good vertical accuracy– Capable of capturing multiple returns Capable of capturing multiple returns
and intensity values from different and intensity values from different parts of objectsparts of objects
– Capable of penetrating openings in Capable of penetrating openings in tree canopies and measuring ground tree canopies and measuring ground elevationelevation
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Aerial Photo (0.3 m, True Color)
How do we identify each house and each tree?
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Constrained 3D K Mutual Nearest Neighborhood Point Segmentation Algorithm
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Incorporating Time And Daily Activities Into An Analysis Of Urban Violent Crime
Or
Measuring Crime Rates Realistically
Janis SchubertPh.D. dissertation, University of Texas at Dallas, 2009
Adviser: Dr. Dan Griffith(currently Senior Research Scientist, Critical Infrastructure
Protection Program, Los Alamos National Laboratory)
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Crime statistics invariably use the residential (night time) population when calculating rates.
This is what the US Census reports.
But the geographic distribution of population varies substantially during any 24 hour period as people go about their daily business (work, shop, play, etc.)
Night time population density Daily Change in Population Density
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Day/Night Aggravated Assault Rates Uses a simulation model of daily traffic flows to estimate population at each location at different times of the dayThen uses crime counts for same locations and time periods to re-calculate crime rates.
10am-4pm 10pm-4am
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Peter V. PennesiCrime Analyst, Plano Police Department
MGIS Graduate UT-Dallas
Application of GIS in Law Enforcement
Enhancing Public Service with Locational Awareness
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Selected Law Enforcement Areas of InterestFor GIS Researchers and Developers
Do home addresses of registered sex offenders cluster?Where are these clusters?(I don’t want to live there!)
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Selected Law Enforcement Areas of InterestFor GIS Researchers and Developers
Where are the hotspots for automobile accidents?Avoid these intersections! Can we redesign them?
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Selected Law Enforcement Areas of InterestFor GIS Researchers and Developers
Hotspot street segments for crime.Police these streets!
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Site SelectionGeographies of opportunity
Leads to a real estate solution
Enhancing Business with Location Intelligence
Wayne GearyStaubach Companies
Advisers and Analysts for the Real Estate Industry
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An Automated System For Image-to-Vector Georeferencing
Yan LiPh.D. dissertation, UT-Dallas 2009
Adviser: Dr. Ronald Briggs(currently GIS Data Base Manager, City of Dallas, Tx.)
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Where in the world is this image
City of Dallas Street Centerline file68,000 street segments
?
Image is distorted and its location is unknown
Finding the location and appropriate transformation to position and align an image at its true world location
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The Problem
The current way of georeferencing: – Manually create a set of control point pairs (CPPs)
linking between the raster image and a reference map
+
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An automated solution is highly desirable
– Difficult, time consuming, tedious, inaccurate, inconsistent
– Often impossible to find locations without prior knowledge
– About the image’s approximate location
– About the region by the operator
GeoInfo 2010, Dr. Yan Li & Dr. Ron Briggs
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Automated Approach
Go Home China Project, June 2010, Dr. Yan Li & Dr. Ron Briggs
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1. Automatic feature
extraction
3. Optimize transformation result
Image Point Set R
Vector Point Set V
2. Automatic
featurematching
An unknown distorted image
A n arbitrarily large reference road network
from Vectorbase
from image
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Methodology searches for similar patterns of road intersections:
must be invariant to the underlying transformation
v0
v1
v2
vi
- +
+ +
+ -- -
axi
ayi
X
Y
a0
For an affine transformation, the ratio of the areas of triangles between intersections is a constant
For a similarity transformation, angles are preserved and distance between two points stay proportional
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Photorealistic Modeling of Geological Formations
Mohammed AlfarhanPh.D. dissertation, UT-Dallas 2010
Adviser: Dr. Carlos Aiken(currently faculty member, King Saud University, Saudi Arabia)
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GeoAnalysis Tool with Surface Extrusions
Not just a movie!It’s a model of the formation from which measurements can be made
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Display and measurements using ArcGIS/ArcMap
A model of the formation from which measurements can be made
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Articles in Chinese• He and Pan Geographical Concentration and
Agglomeration of Industries Progress in Geography, Vol. 26, No. 2, 2007 pp 1-13
– Uses Ripley’s K-function
• Wei, Zhang and Chen Study on Construction Land Distribution using Spatial Autocorrelation AnalysisProgress in Geography, Vol. 26, No. 3, 2007 pp 1-17– Uses Moran’s I
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I have really enjoyed being here.
I hope that you have learned some new and useful things!
www.utdallas.edu/~briggs