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. 239 257(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

Footprint (top of building)

Data

Buffer (ground) • • • • 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

Model

•

P=a*A b

research allometric growth model used in previous Population • • Area – Population is an increasing function of area (A)

P=α*V β

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 R 2 /Pseudo AIC R 2

Building volume based 0.844

131.04

Building area based 0.812

139.41

OLS

Land use area based 0.638

207.88

Road length based 0.619

185.48

Building volume based 0.850

128.84

Building area based 0.824 138.96

SPATIAL MODELS

Land use area based 0.674 189.61

• • 0.72 Spatial always better than OLS 178.55

RMSE

28.415

53.581

53.622

244.50

28.173

35.072

53.884 74.770 0.484 0.44 0.546

Adj RMSE

0.4023

0.7268

0.4381

0.909

0.288

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

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

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 Characteristics LIDAR – 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) 27 How do we identify each house and each tree?

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

Crime statistics invariably use the residential (night time) population when calculating rates.

This is what the US Census reports.

Daily Change in Population Density

But the geographic distribution of population varies substantially during any 24 hour period as people go about their daily business (work, shop, play, etc.)

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

Image Point Set

R

2. Automatic feature matching 3. Optimize transformation result 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

- - +

v 0 v 2 a yi a xi a 0 v 1

+ -

v i

+ +

X

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!

[email protected]

www.utdallas.edu/~briggs