Using Spatial Statistics in Research: Examples from work at UT-Dallas

Using Spatial Statistics in Research:Examples from work at UT-Dallas

Faculty researchPh.D. dissertationsMasters 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. 239-257(19)

*associate professor**assistant professor***Ph.D. candidateUniversity 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

Population1 - 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

5

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

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)

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

  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

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.

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

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

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

ApplicationsBy

Gene A. FeighnyPh.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 2010Adviser: Dr Fang Qiu

(Currently, Assistant Professor, Florida Atlantic University)

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.

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

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

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?

28

Constrained 3D K Mutual Nearest Neighborhood Point Segmentation Algorithm

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)

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

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

Peter V. PennesiCrime Analyst, Plano Police Department

MGIS Graduate UT-Dallas

Application of GIS in Law Enforcement

Enhancing Public Service with Locational Awareness

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!)

Selected Law Enforcement Areas of InterestFor GIS Researchers and Developers

Where are the hotspots for automobile accidents?Avoid these intersections! Can we redesign them?

Selected Law Enforcement Areas of InterestFor GIS Researchers and Developers

Hotspot street segments for crime.Police these streets!

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

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.)

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

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

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

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

Photorealistic Modeling of Geological Formations

Mohammed AlfarhanPh.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

Display and measurements using ArcGIS/ArcMap

A model of the formation from which measurements can be made

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

I have really enjoyed being here.

I hope that you have learned some new and useful things!

briggs@utdallas.edu

www.utdallas.edu/~briggs