Using Functional Distance Measures When Calibrating Journey-To-Crime Distance Decay Algorithms

Total Page:16

File Type:pdf, Size:1020Kb

Using Functional Distance Measures When Calibrating Journey-To-Crime Distance Decay Algorithms USING FUNCTIONAL DISTANCE MEASURES WHEN CALIBRATING JOURNEY-TO-CRIME DISTANCE DECAY ALGORITHMS A Thesis Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Master of Natural Sciences in The Interdepartmental Program of Natural Sciences by Joshua David Kent B.S., Louisiana State University, 1994 December 2003 ACKNOWLEDGMENTS The work reported in this research was partially supported by the efforts of Dr. James Mitchell of the Louisiana Department of Transportation and Development - Information Technology section for donating portions of the Geographic Data Technology (GDT) Dynamap®-Transportation data set. Additional thanks are paid for the thirty-five residence of East Baton Rouge Parish who graciously supplied the travel data necessary for the successful completion of this study. The author also wishes to acknowledge the support expressed by Dr. Michael Leitner, Dr. Andrew Curtis, Mr. DeWitt Braud, and Dr. Frank Cartledge - their efforts helped to make this thesis possible. Finally, the author thanks his wonderful wife and supportive family for their encouragement and tolerance. ii TABLE OF CONTENTS ACKNOWLEDGMENTS .............................................................................................................. ii LIST OF TABLES.......................................................................................................................... v LIST OF FIGURES ....................................................................................................................... vi ABSTRACT.................................................................................................................................. vii CHAPTER 1. INTRODUCTION ................................................................................................... 1 CHAPTER 2. REVIEW OF LITERATURE................................................................................ 11 2.1 Perception of Distance and Space within Behavioral Geography ...................................... 12 2.2 Concepts in Environmental Criminology ........................................................................... 17 2.3 Journey-to-Crime Modeling ............................................................................................... 23 2.4 Distance Decay Functions .................................................................................................. 30 2.5 Geographic Profiling........................................................................................................... 34 2.6 Characteristics of a Serial Offender.................................................................................... 48 CHAPTER 3. MATERIALS AND METHODS .......................................................................... 52 3.1 Modeling Environment....................................................................................................... 55 3.2 Test Subjects....................................................................................................................... 58 3.3 Data Sets ............................................................................................................................. 60 3.4 The Geographic Profile Procedure ..................................................................................... 62 3.4.1 Calibration Routine...................................................................................................... 62 3.4.2 Journey-to-Crime Routine ........................................................................................... 65 3.5 Determining the Best-Fit Distance Decay Model............................................................... 67 CHAPTER 4. RESULTS AND DISCUSSION............................................................................ 69 4.1 Frequency Distributions by Metric..................................................................................... 69 4.2 Journey-To-Crime Calibration Models............................................................................... 73 4.2.1 Euclidean Distance Decay Model................................................................................ 73 4.2.2 Travel-Path Distance Decay Model............................................................................. 77 4.2.3 Temporal-Path Distance Decay Model........................................................................ 77 4.3 Assumptions........................................................................................................................ 78 4.4 Best Performing Geoprofiles .............................................................................................. 78 4.5 Geographic Profiles by Euclidean Metric........................................................................... 85 4.5.1 Straight-Line Path Distances........................................................................................ 86 4.5.2 Manhattan Distance ..................................................................................................... 86 4.6 Functional Distance Measures............................................................................................ 87 4.6.1 Travel-Path Distances .................................................................................................. 87 4.6.2 Temporal-Path Distances............................................................................................. 88 4.7 Further Discussion .............................................................................................................. 89 iii CHAPTER 5. SUMMARY AND CONCLUSIONS.................................................................... 96 5.1 Conclusions......................................................................................................................... 98 5.2 Recommendations............................................................................................................. 101 5.3 Further Topics of Study .................................................................................................... 103 REFERENCES ........................................................................................................................... 107 APPENDIX A TRAVEL DIARY QUESTIONNAIRE ............................................................. 111 APPENDIX B COMMUTER SURVEY.................................................................................... 112 VITA........................................................................................................................................... 114 iv LIST OF TABLES Table 4.1 Geographic Profiling Results: Best Performing Geoprofiles ....................................... 79 Table 4.2 Geographic Profile Results: Direct Path Euclidean Metric .......................................... 85 Table 4.3 Geographic Profile Results: Manhattan Euclidean Metric ........................................... 86 Table 4.4 Geographic Profile Results: Travel-Path Functional Distance Metric ......................... 87 Table 4.5 Geographic Profile Results: Temporal-Path Functional Distance Metric .................... 88 Table 4.6 Geographic Profiling Results: Cumulative Results by Metric ..................................... 89 Table 4.7 Commuter Survey Results ............................................................................................ 92 v LIST OF FIGURES Figure 1.1 Travel Structure Characteristics .................................................................................. 7 Figure 1.2 Travel Cost Paradox .................................................................................................... 9 Figure 2.1 Routine Activity Theory .............................................................................................19 Figure 2.2 Journey-to-Crime Distance Decay Curves ................................................................. 32 Figure 2.3 Negative Exponential Functions ................................................................................. 41 Figure 3.1 Mathematical Calibration Algorithms .........................................................................65 Figure 3.2 Geographic Profiling Metric Calibration Matrix ........................................................ 67 Figure 4.1 Frequency Distributions By Metric .............................................................................70 Figure 4.2 Calibrated Distance Decay Algorithms for Euclidean Distance Metrics ....................74 Figure 4.3 Calibrated Distance Decay Algorithms for Travel-Path Functional Distance Metrics ......................................................................................................... 75 Figure 4.4 Calibrated Distance Decay Algorithms for Temporal-Path Functional Distance Metrics ......................................................................................................... 76 Figure 4.5 Geographic Profiling Results: Best Performing Geoprofiles – Test-Sample 1 ...........81 Figure 4.6 Geographic Profiling Results: Best Performing Geoprofiles – Test-Sample 2 ...........82 Figure 4.7 Geographic Profiling Results: Best Performing Geoprofiles – Test-Sample 3 ...........83 Figure 4.8 Geographic Profiling Results: Best Performing Geoprofiles – Test-Sample 4 ...........84 Figure 4.9 Potential Edge Effects When Using Artificial Boundaries ......................................... 93 vi ABSTRACT Spatial analysis has long been a valuable
Recommended publications
  • FUNCTIONAL ANALYSIS 1. Banach and Hilbert Spaces in What
    FUNCTIONAL ANALYSIS PIOTR HAJLASZ 1. Banach and Hilbert spaces In what follows K will denote R of C. Definition. A normed space is a pair (X, k · k), where X is a linear space over K and k · k : X → [0, ∞) is a function, called a norm, such that (1) kx + yk ≤ kxk + kyk for all x, y ∈ X; (2) kαxk = |α|kxk for all x ∈ X and α ∈ K; (3) kxk = 0 if and only if x = 0. Since kx − yk ≤ kx − zk + kz − yk for all x, y, z ∈ X, d(x, y) = kx − yk defines a metric in a normed space. In what follows normed paces will always be regarded as metric spaces with respect to the metric d. A normed space is called a Banach space if it is complete with respect to the metric d. Definition. Let X be a linear space over K (=R or C). The inner product (scalar product) is a function h·, ·i : X × X → K such that (1) hx, xi ≥ 0; (2) hx, xi = 0 if and only if x = 0; (3) hαx, yi = αhx, yi; (4) hx1 + x2, yi = hx1, yi + hx2, yi; (5) hx, yi = hy, xi, for all x, x1, x2, y ∈ X and all α ∈ K. As an obvious corollary we obtain hx, y1 + y2i = hx, y1i + hx, y2i, hx, αyi = αhx, yi , Date: February 12, 2009. 1 2 PIOTR HAJLASZ for all x, y1, y2 ∈ X and α ∈ K. For a space with an inner product we define kxk = phx, xi . Lemma 1.1 (Schwarz inequality).
    [Show full text]
  • Linear Spaces
    Chapter 2 Linear Spaces Contents FieldofScalars ........................................ ............. 2.2 VectorSpaces ........................................ .............. 2.3 Subspaces .......................................... .............. 2.5 Sumofsubsets........................................ .............. 2.5 Linearcombinations..................................... .............. 2.6 Linearindependence................................... ................ 2.7 BasisandDimension ..................................... ............. 2.7 Convexity ............................................ ............ 2.8 Normedlinearspaces ................................... ............... 2.9 The `p and Lp spaces ............................................. 2.10 Topologicalconcepts ................................... ............... 2.12 Opensets ............................................ ............ 2.13 Closedsets........................................... ............. 2.14 Boundedsets......................................... .............. 2.15 Convergence of sequences . ................... 2.16 Series .............................................. ............ 2.17 Cauchysequences .................................... ................ 2.18 Banachspaces....................................... ............... 2.19 Completesubsets ....................................... ............. 2.19 Transformations ...................................... ............... 2.21 Lineartransformations.................................. ................ 2.21
    [Show full text]
  • General Inner Product & Fourier Series
    General Inner Products 1 General Inner Product & Fourier Series Advanced Topics in Linear Algebra, Spring 2014 Cameron Braithwaite 1 General Inner Product The inner product is an algebraic operation that takes two vectors of equal length and com- putes a single number, a scalar. It introduces a geometric intuition for length and angles of vectors. The inner product is a generalization of the dot product which is the more familiar operation that's specific to the field of real numbers only. Euclidean space which is limited to 2 and 3 dimensions uses the dot product. The inner product is a structure that generalizes to vector spaces of any dimension. The utility of the inner product is very significant when proving theorems and runing computations. An important aspect that can be derived is the notion of convergence. Building on convergence we can move to represenations of functions, specifally periodic functions which show up frequently. The Fourier series is an expansion of periodic functions specific to sines and cosines. By the end of this paper we will be able to use a Fourier series to represent a wave function. To build toward this result many theorems are stated and only a few have proofs while some proofs are trivial and left for the reader to save time and space. Definition 1.11 Real Inner Product Let V be a real vector space and a; b 2 V . An inner product on V is a function h ; i : V x V ! R satisfying the following conditions: (a) hαa + α0b; ci = αha; ci + α0hb; ci (b) hc; αa + α0bi = αhc; ai + α0hc; bi (c) ha; bi = hb; ai (d) ha; aiis a positive real number for any a 6= 0 Definition 1.12 Complex Inner Product Let V be a vector space.
    [Show full text]
  • Function Space Tensor Decomposition and Its Application in Sports Analytics
    East Tennessee State University Digital Commons @ East Tennessee State University Electronic Theses and Dissertations Student Works 12-2019 Function Space Tensor Decomposition and its Application in Sports Analytics Justin Reising East Tennessee State University Follow this and additional works at: https://dc.etsu.edu/etd Part of the Applied Statistics Commons, Multivariate Analysis Commons, and the Other Applied Mathematics Commons Recommended Citation Reising, Justin, "Function Space Tensor Decomposition and its Application in Sports Analytics" (2019). Electronic Theses and Dissertations. Paper 3676. https://dc.etsu.edu/etd/3676 This Thesis - Open Access is brought to you for free and open access by the Student Works at Digital Commons @ East Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons @ East Tennessee State University. For more information, please contact [email protected]. Function Space Tensor Decomposition and its Application in Sports Analytics A thesis presented to the faculty of the Department of Mathematics East Tennessee State University In partial fulfillment of the requirements for the degree Master of Science in Mathematical Sciences by Justin Reising December 2019 Jeff Knisley, Ph.D. Nicole Lewis, Ph.D. Michele Joyner, Ph.D. Keywords: Sports Analytics, PCA, Tensor Decomposition, Functional Analysis. ABSTRACT Function Space Tensor Decomposition and its Application in Sports Analytics by Justin Reising Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition.
    [Show full text]
  • Calculus of Variations
    MIT OpenCourseWare http://ocw.mit.edu 16.323 Principles of Optimal Control Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 16.323 Lecture 5 Calculus of Variations • Calculus of Variations • Most books cover this material well, but Kirk Chapter 4 does a particularly nice job. • See here for online reference. x(t) x*+ αδx(1) x*- αδx(1) x* αδx(1) −αδx(1) t t0 tf Figure by MIT OpenCourseWare. Spr 2008 16.323 5–1 Calculus of Variations • Goal: Develop alternative approach to solve general optimization problems for continuous systems – variational calculus – Formal approach will provide new insights for constrained solutions, and a more direct path to the solution for other problems. • Main issue – General control problem, the cost is a function of functions x(t) and u(t). � tf min J = h(x(tf )) + g(x(t), u(t), t)) dt t0 subject to x˙ = f(x, u, t) x(t0), t0 given m(x(tf ), tf ) = 0 – Call J(x(t), u(t)) a functional. • Need to investigate how to find the optimal values of a functional. – For a function, we found the gradient, and set it to zero to find the stationary points, and then investigated the higher order derivatives to determine if it is a maximum or minimum. – Will investigate something similar for functionals. June 18, 2008 Spr 2008 16.323 5–2 • Maximum and Minimum of a Function – A function f(x) has a local minimum at x� if f(x) ≥ f(x �) for all admissible x in �x − x�� ≤ � – Minimum can occur at (i) stationary point, (ii) at a boundary, or (iii) a point of discontinuous derivative.
    [Show full text]
  • Fact Sheet Functional Analysis
    Fact Sheet Functional Analysis Literature: Hackbusch, W.: Theorie und Numerik elliptischer Differentialgleichungen. Teubner, 1986. Knabner, P., Angermann, L.: Numerik partieller Differentialgleichungen. Springer, 2000. Triebel, H.: H¨ohere Analysis. Harri Deutsch, 1980. Dobrowolski, M.: Angewandte Funktionalanalysis, Springer, 2010. 1. Banach- and Hilbert spaces Let V be a real vector space. Normed space: A norm is a mapping k · k : V ! [0; 1), such that: kuk = 0 , u = 0; (definiteness) kαuk = jαj · kuk; α 2 R; u 2 V; (positive scalability) ku + vk ≤ kuk + kvk; u; v 2 V: (triangle inequality) The pairing (V; k · k) is called a normed space. Seminorm: In contrast to a norm there may be elements u 6= 0 such that kuk = 0. It still holds kuk = 0 if u = 0. Comparison of two norms: Two norms k · k1, k · k2 are called equivalent if there is a constant C such that: −1 C kuk1 ≤ kuk2 ≤ Ckuk1; u 2 V: If only one of these inequalities can be fulfilled, e.g. kuk2 ≤ Ckuk1; u 2 V; the norm k · k1 is called stronger than the norm k · k2. k · k2 is called weaker than k · k1. Topology: In every normed space a canonical topology can be defined. A subset U ⊂ V is called open if for every u 2 U there exists a " > 0 such that B"(u) = fv 2 V : ku − vk < "g ⊂ U: Convergence: A sequence vn converges to v w.r.t. the norm k · k if lim kvn − vk = 0: n!1 1 A sequence vn ⊂ V is called Cauchy sequence, if supfkvn − vmk : n; m ≥ kg ! 0 for k ! 1.
    [Show full text]
  • Inner Product Spaces
    CHAPTER 6 Woman teaching geometry, from a fourteenth-century edition of Euclid’s geometry book. Inner Product Spaces In making the definition of a vector space, we generalized the linear structure (addition and scalar multiplication) of R2 and R3. We ignored other important features, such as the notions of length and angle. These ideas are embedded in the concept we now investigate, inner products. Our standing assumptions are as follows: 6.1 Notation F, V F denotes R or C. V denotes a vector space over F. LEARNING OBJECTIVES FOR THIS CHAPTER Cauchy–Schwarz Inequality Gram–Schmidt Procedure linear functionals on inner product spaces calculating minimum distance to a subspace Linear Algebra Done Right, third edition, by Sheldon Axler 164 CHAPTER 6 Inner Product Spaces 6.A Inner Products and Norms Inner Products To motivate the concept of inner prod- 2 3 x1 , x 2 uct, think of vectors in R and R as x arrows with initial point at the origin. x R2 R3 H L The length of a vector in or is called the norm of x, denoted x . 2 k k Thus for x .x1; x2/ R , we have The length of this vector x is p D2 2 2 x x1 x2 . p 2 2 x1 x2 . k k D C 3 C Similarly, if x .x1; x2; x3/ R , p 2D 2 2 2 then x x1 x2 x3 . k k D C C Even though we cannot draw pictures in higher dimensions, the gener- n n alization to R is obvious: we define the norm of x .x1; : : : ; xn/ R D 2 by p 2 2 x x1 xn : k k D C C The norm is not linear on Rn.
    [Show full text]
  • Calculus of Variations Raju K George, IIST
    Calculus of Variations Raju K George, IIST Lecture-1 In Calculus of Variations, we will study maximum and minimum of a certain class of functions. We first recall some maxima/minima results from the classical calculus. Maxima and Minima Let X and Y be two arbitrary sets and f : X Y be a well-defined function having domain → X and range Y . The function values f(x) become comparable if Y is IR or a subset of IR. Thus, optimization problem is valid for real valued functions. Let f : X IR be a real valued → function having X as its domain. Now x X is said to be maximum point for the function f if 0 ∈ f(x ) f(x) x X. The value f(x ) is called the maximum value of f. Similarly, x X is 0 ≥ ∀ ∈ 0 0 ∈ said to be a minimum point for the function f if f(x ) f(x) x X and in this case f(x ) is 0 ≤ ∀ ∈ 0 the minimum value of f. Sufficient condition for having maximum and minimum: Theorem (Weierstrass Theorem) Let S IR and f : S IR be a well defined function. Then f will have a maximum/minimum ⊆ → under the following sufficient conditions. 1. f : S IR is a continuous function. → 2. S IR is a bound and closed (compact) subset of IR. ⊂ Note that the above conditions are just sufficient conditions but not necessary. Example 1: Let f : [ 1, 1] IR defined by − → 1 x = 0 f(x)= − x x = 0 | | 6 1 −1 +1 −1 Obviously f(x) is not continuous at x = 0.
    [Show full text]
  • Collaborative Fashion Recommendation: a Functional Tensor Factorization Approach
    Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach Yang Hu†, Xi Yi‡, Larry S. Davis§ Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742 [email protected]†, [email protected]‡, [email protected]§ ABSTRACT With the rapid expansion of online shopping for fashion products, effective fashion recommendation has become an increasingly important problem. In this work, we study the problem of personalized outfit recommendation, i.e. auto- matically suggesting outfits to users that fit their personal (a) Users 1 fashion preferences. Unlike existing recommendation sys- tems that usually recommend individual items, we suggest sets of items, which interact with each other, to users. We propose a functional tensor factorization method to model the interactions between user and fashion items. To effec- tively utilize the multi-modal features of the fashion items, we use a gradient boosting based method to learn nonlinear (b) Users 2 functions to map the feature vectors from the feature space into some low dimensional latent space. The effectiveness of the proposed algorithm is validated through extensive ex- periments on real world user data from a popular fashion- focused social network. Categories and Subject Descriptors (c) Users 3 H.3.3 [Information Search and Retrieval]: Retrieval models; I.2.6 [Learning]: Knowledge acquisition Figure 1: Examples of fashion sets created by three Keywords Polyvore users. Different users have different style preferences. Our task is to automatically recom- Recommendation systems; Collaborative filtering; Tensor mend outfits to users that fit their personal taste. factorization; Learning to rank; Gradient boosting 1. INTRODUCTION book3, where people showcase their personal styles and con- With the proliferation of social networks, people share al- nect to others that share similar fashion taste.
    [Show full text]
  • Functional and Structured Tensor Analysis for Engineers
    UNM BOOK DRAFT September 4, 2003 5:21 pm Functional and Structured Tensor Analysis for Engineers A casual (intuition-based) introduction to vector and tensor analysis with reviews of popular notations used in contemporary materials modeling R. M. Brannon University of New Mexico, Albuquerque Copyright is reserved. Individual copies may be made for personal use. No part of this document may be reproduced for profit. Contact author at [email protected] NOTE: When using Adobe’s “acrobat reader” to view this document, the page numbers in acrobat will not coincide with the page numbers shown at the bottom of each page of this document. Note to draft readers: The most useful textbooks are the ones with fantastic indexes. The book’s index is rather new and still under construction. It would really help if you all could send me a note whenever you discover that an important entry is miss- ing from this index. I’ll be sure to add it. This work is a community effort. Let’s try to make this document helpful to others. FUNCTIONAL AND STRUCTURED TENSOR ANALYSIS FOR ENGINEERS A casual (intuition-based) introduction to vector and tensor analysis with reviews of popular notations used in contemporary materials modeling Rebecca M. Brannon† †University of New Mexico Adjunct professor [email protected] Abstract Elementary vector and tensor analysis concepts are reviewed in a manner that proves useful for higher-order tensor analysis of anisotropic media. In addition to reviewing basic matrix and vector analysis, the concept of a tensor is cov- ered by reviewing and contrasting numerous different definition one might see in the literature for the term “tensor.” Basic vector and tensor operations are provided, as well as some lesser-known operations that are useful in materials modeling.
    [Show full text]
  • An Introduction to Some Aspects of Functional Analysis
    An introduction to some aspects of functional analysis Stephen Semmes Rice University Abstract These informal notes deal with some very basic objects in functional analysis, including norms and seminorms on vector spaces, bounded linear operators, and dual spaces of bounded linear functionals in particular. Contents 1 Norms on vector spaces 3 2 Convexity 4 3 Inner product spaces 5 4 A few simple estimates 7 5 Summable functions 8 6 p-Summable functions 9 7 p =2 10 8 Bounded continuous functions 11 9 Integral norms 12 10 Completeness 13 11 Bounded linear mappings 14 12 Continuous extensions 15 13 Bounded linear mappings, 2 15 14 Bounded linear functionals 16 1 15 ℓ1(E)∗ 18 ∗ 16 c0(E) 18 17 H¨older’s inequality 20 18 ℓp(E)∗ 20 19 Separability 22 20 An extension lemma 22 21 The Hahn–Banach theorem 23 22 Complex vector spaces 24 23 The weak∗ topology 24 24 Seminorms 25 25 The weak∗ topology, 2 26 26 The Banach–Alaoglu theorem 27 27 The weak topology 28 28 Seminorms, 2 28 29 Convergent sequences 30 30 Uniform boundedness 31 31 Examples 32 32 An embedding 33 33 Induced mappings 33 34 Continuity of norms 34 35 Infinite series 35 36 Some examples 36 37 Quotient spaces 37 38 Quotients and duality 38 39 Dual mappings 39 2 40 Second duals 39 41 Continuous functions 41 42 Bounded sets 42 43 Hahn–Banach, revisited 43 References 44 1 Norms on vector spaces Let V be a vector space over the real numbers R or the complex numbers C.
    [Show full text]
  • The Sparse Manifold Transform
    The Sparse Manifold Transform Yubei Chen1,2 Dylan M Paiton1,3 Bruno A Olshausen1,3,4 1Redwood Center for Theoretical Neuroscience 2Department of Electrical Engineering and Computer Science 3Vision Science Graduate Group 4Helen Wills Neuroscience Institute & School of Optometry University of California, Berkeley Berkeley, CA 94720 [email protected] Abstract We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maintaining approximate invertibility. The sparse manifold transform is an unsupervised and generative framework that explicitly and simultaneously models the sparse dis- creteness and low-dimensional manifold structure found in natural scenes. When stacked, it also models hierarchical composition. We provide a theoretical descrip- tion of the transform and demonstrate properties of the learned representation on both synthetic data and natural videos. 1 Introduction Inspired by Pattern Theory [40], we attempt to model three important and pervasive patterns in natural signals: sparse discreteness, low dimensional manifold structure and hierarchical composition. Each of these concepts have been individually explored in previous studies. For example, sparse coding [43, 44] and ICA [5, 28] can learn sparse and discrete elements that make up natural signals. Manifold learning [56, 48, 38, 4] was proposed to model and visualize low-dimensional continuous transforms such as smooth 3D rotations or translations of a single discrete element. Deformable, compositional models [60, 18] allow for a hierarchical composition of components into a more abstract representation.
    [Show full text]