Using Spatial Statistics in Research:
Examples from work at UT-Dallas
Faculty research
Ph.D. dissertations
Masters Projects
Former UTD graduates “at work”
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. 239257(19)
*associate professor
**assistant professor
***Ph.D. candidate
University of Texas at Dallas
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
Population
1 - 50
51 - 125
126 - 200
201 - 400
>400
500 m
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
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
5
Data
Footprint
(top of building)
• 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
Buffer
elevation
within
buffer
(ground)
(ground around building)
• Area x height = volume
Model
• P=a*Ab allometric growth model used in previous
research
Population
Area
– 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
Results
Models
R2/Pseudo
AIC
RMSE
R2
Building volume based
Building area based
OLS
Land use area based
Road length based
Building volume based
Building area based
SPATIAL
MODELS
Land use area based
0.844
0.812
Adj
RMSE
131.04
139.41
28.415
0.4023
53.581
0.7268
53.622
0.4381
244.50
0.909
28.173
0.288
0.638
207.88
0.619
185.48
0.850
128.84
0.824
138.96
35.072
0.484
0.674
189.61
53.884
0.44
length based
• Volume Road
always
better than
area
or road
0.72
178.55
74.770
• Spatial always better than OLS
0.546
Case study:
A Spatial Analysis of West Nile Virus
Diffusion of WNV across the US
Daniel A. Griffith
Ashbel Smith Professor
http://www.ij-healthgeographics.com/articles/browse.asp
A 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.
Geographic distribution of West
Nile virus (WNV) reported cases
in 2002. Black denotes states
with, and white denotes states
without reported cases.
% WNV
deaths in
2003
% WNV
deaths in
2004
2002
Challenges of spatial statistics in
analyzing WNV
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?
• People are dying.
How are these issues being addressed?
• Specifying correct spatial statistical models.
Scatterplots of observed versus
predicted values
Surprising spatial filter result: a jump to
California
A Predictive Terrestrial Clutter
Model for Ground-to-Ground
Automated Target Detection
Applications
By
Gene A. Feighny
Ph.D. dissertation, UT-Dallas 2010
Adviser: Dr. Denis Dean
(currently Senior Research Engineer, E-Systems Inc.)
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”?
• 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
• What are some of the characteristics of clutter?
– degree of spatial clustering at various distances.
• How do we measure this?
– Ripley’s K function
URBAN FOREST INVENTORY
USING AIRBORNE LIDAR DATA
AND
HYPERSPECTRAL IMAGERY
by
Caiyun Zhang
Ph.D. dissertation, UT-Dallas 2010
Adviser: Dr Fang Qiu
(Currently, Assistant Professor, Florida Atlantic University)
Research Objectives
1.
2.
3.
4.
5.
Develop a relatively simple and robust algorithm to isolate individual
trees using LiDAR vector point cloud data.
Estimate single tree metrics such as tree heights, tree distributions, stem
density, crown diameters, crown depths, and base heights, from original
LiDAR vector data.
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.
Produce urban forest 3-D scenes by constructing 3-D tree visualization
models using the LiDAR derived information.
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.
Lidar produces a 3-D “point cloud”
Various cluster analysis techniques are used to identify different objects
Turtle Creek, Dallas:
Lidar data (laser derived elevations) identifies trees
• Ground Points
Turtle Creek, Dallas:
Hyperspectral data (2151 bands) identifies species
• Ground Points
Accuracy doubled from existing methods:
--60%-70% versus 30%-40%
--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
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
Real trees in 2-D image
Crown diameter
Proposal for Dissertation
Supervising Committee:
Dr. Ronald Briggs
Dr. Yongwan Chun
Dr. Denis Dean
Dr. Fang Qiu (Chair)
Point Cloud Segmentation-based Filtering and
Object-based Feature Extraction from Airborne
LiDAR Data
Jie Chang
Ph.D. Program in Geospatial Sciences
University of Texas at Dallas
May 3, 2010
• LIDAR
LiDAR Characteristics
– 3D remote sensing
– Direct 3D position measurements
– Very good vertical accuracy
– Capable of capturing multiple
returns and intensity values
from different parts of objects
– Capable of penetrating openings
in tree canopies and measuring
ground elevation
26
Aerial Photo (0.3 m, True Color)
How do we identify each house and each tree?
27
Constrained 3D K Mutual Nearest Neighborhood
Point Segmentation Algorithm
28
Incorporating Time And Daily Activities
Into An Analysis Of Urban Violent Crime
Or
Measuring Crime Rates Realistically
Janis Schubert
Ph.D. dissertation, University of Texas at Dallas, 2009
Adviser: Dr. Dan Griffith
(currently Senior Research Scientist, Critical Infrastructure
Protection Program, Los Alamos National Laboratory)
Night time population density
Daily Change in Population Density
Crime statistics invariably use the
residential (night time) population
when calculating rates.
But the geographic distribution of
population varies substantially
during any 24 hour period as
people go about their daily
business (work, shop, play, etc.)
This is what the US Census reports.
10am-4pm
10pm-4am
Day/Night Aggravated Assault Rates
Uses a simulation model of daily traffic flows to estimate population at each
location at different times of the day
Then uses crime counts for same locations and time periods to re-calculate
crime rates.
Application of GIS in Law Enforcement
Peter V. Pennesi
Crime Analyst, Plano Police Department
MGIS Graduate UT-Dallas
Enhancing Public Service with Locational Awareness
Do home addresses of registered sex offenders cluster?
Where are these clusters?
(I don’t want to live there!)
Selected Law Enforcement Areas of Interest
For GIS Researchers and Developers
Where are the hotspots for automobile accidents?
Avoid these intersections! Can we redesign them?
Selected Law Enforcement Areas of Interest
For GIS Researchers and Developers
Hotspot street segments for crime.
Police these streets!
Selected Law Enforcement Areas of Interest
For GIS Researchers and Developers
Enhancing Business with Location Intelligence
Wayne Geary
Staubach Companies
Advisers and Analysts for the Real Estate Industry
Site Selection
Geographies of opportunity
Leads to a real estate solution
An Automated System For
Image-to-Vector Georeferencing
Yan Li
Ph.D. dissertation, UT-Dallas 2009
Adviser: Dr. Ronald Briggs
(currently GIS Data Base Manager, City of Dallas, Tx.)
Finding the location and appropriate transformation to
position and align an image at its true world location
Image is distorted and its
location is unknown
Where in the world is this image
?
City of Dallas Street Centerline file
68,000 street segments
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
– 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
+
An automated solution is highly desirable
GeoInfo 2010, Dr. Yan Li & Dr. Ron Briggs
4
2
Automated Approach
from
image
An unknown
distorted image
1. Automatic
feature
extraction
3. Optimize
transformation
result
Image Point
2. Automatic
Set R
feature
matching
from
Vector
base
A n arbitrarily large
reference road network
Vector Point Set V
Go Home China Project, June 2010, Dr.
Yan Li & Dr. Ron Briggs
43
Methodology searches for similar patterns of
road intersections:
must be invariant to the underlying transformation
Y
vi
v2
++
ayi
axi
-+
a0
X
v1
v0
--
For a similarity transformation,
angles are preserved and distance
between two points stay
proportional
+-
For an affine transformation, the ratio of
the areas of triangles between
intersections is a constant
Photorealistic Modeling of
Geological Formations
Mohammed Alfarhan
Ph.D. dissertation, UT-Dallas 2010
Adviser: Dr. Carlos Aiken
(currently faculty member, King Saud University, Saudi Arabia)
GeoAnalysis Tool with Surface Extrusions
Not just a movie!
It’s a model of the formation from which measurements can be made
A model of the formation from which measurements can be made
Display and measurements using ArcGIS/ArcMap
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 Analysis
Progress in Geography, Vol. 26, No. 3, 2007 pp
1-17
– Uses Moran’s I
I have really enjoyed being here.
I hope that you have learned some new
and useful things!
briggs@utdallas.edu
www.utdallas.edu/~briggs