1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting 4.5 Evaluating the Performance of a Classifier 4.6 Methods for Comparing Classifiers 4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers 5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem 5.8 Multiclass Problem 5.9 Bibliographic Notes 5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets 6.5 Alternative Methods for Generating Frequent Itemsets 6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution 6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms 9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries 10.2 Statistical Approaches 10.3 Proximity-Based Outlier Detection 10.4 Density-Based Outlier Detection 10.5 Clustering-Based Techniques 10.6 Bibliographic Notes 10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality Reduction Appendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author Index Subject Index

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Introduction to Data Mining

Statistics for Spatial Data.

http://www.ced.berkeley.edu/downloads/research/LUP.parks.pdf

Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’

Transactions of the Institute of British Geographers

Definition: To what extent does the study allow us to draw conclusions about a causal effect between two or more constructs? Issues: Selection, maturation, history, mortality, testing, regression towrd the mean, selection by maturation, treatment by mortality, treatment by testing, measured treatment variables Increase: Eliminate the threats, above all do experimental manipulations, random assignment, and counterbalancing.

Reliability and validity

Aggregated demographic datasets are associated with analytical and cartographic problems due to the arbitrary nature of areal unit partitioning. This article describes a methodology for generating a surface-based representation of population that mitigates these problems. This methodology uses dasymetric mapping and incorporates areal weighting and empirical sampling techniques to assess the relationship between categorical ancillary data and population distribution. As a demonstration, a 100-meter-resolution population surface is generated from U.S. Census block group data for the southeast Pennsylvania region. Remote-sensing-derived urban land-cover data serve as ancillary data in the dasymetric mapping. ∗The author would like to thank Cory Eicher and Alan MacEacren for many helpful conversations regarding dasymetric mapping and Barbara Buttenfield for her constructive comments on a previous draft of this article.

Generating Surface Models of Population Using Dasymetric Mapping

This research presents a new "intelligent" dasymetric mapping technique (IDM), which combines an analyst's domain knowledge with a data-driven methodology to specify the functional relationship of the ancillary classes with the underlying statistical surface being mapped. The data-driven component of IDM employs a flexible empirical sampling approach to acquire information on the data densities of individual ancillary classes, and it uses the ratio of class densities to redistribute population to sub-source zone areas. A summary statistics table characterizing the resulting dasymetric map can be used to compare the quality of the output of different IDM parameterizations. A case study of four population variables is used to demonstrate IDM and provide a visual and quantitative error assessment comparing various IDM parameterizations with areal weighting and conventional "binary" dasymetric mapping. Intelligent dasymetric mapping outperforms areal weighting, and certain IDM parameterizations outperform bin...

Intelligent Dasymetric Mapping and Its Application to Areal Interpolation

Spatial data mining and geographic knowledge discovery—An introduction

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadequately illustrating the spatial distribution of the sign, magnitude, and significance of the influence of each explanatory variable on the dependent variable. Approaches for improving mapping of the results of GWR are illustrated using a case study analysis of population density–median home value relationships in Philadelphia, Pennsylvania, USA. These approaches employ data classification schemes informed by the (nonspatial) data distribution, diverging colour schemes, and bivariate choropleth mapping.

/pdf/mapping-the-results-of-geographically-weighted-regression-5ehvzt1tz2.pdf

Mapping the Results of Geographically Weighted Regression

Conventional multivariate regression can hide important local variations in the relationships among independent and dependent variables in models of environmental equity. Geographically weighted regression (GWR), in combination with choropleth mapping, can reveal this spatial nonstationarity and shed light on its form. We use GWR, in combination with conventional univariate and multivariate statistics, to model the density of air toxic releases in New Jersey, as listed in the U.S. Environmental Protection Agency's Toxic Release Inventory (TRI). The GWR analysis shows that the relationships among race, class, employment, urban concentration, and land use with air toxic release density in New Jersey vary significantly over space. Generally, there is a positively significant relationship of minorities with air toxic releases over a large swath of urban and suburban New Jersey, although this pattern is not evident for all urban areas. Northeast New Jersey, the most densely populated part of the state...

Jeremy Mennis

Papers

Generating Surface Models of Population Using Dasymetric Mapping

Intelligent Dasymetric Mapping and Its Application to Areal Interpolation

Spatial data mining and geographic knowledge discovery—An introduction

Mapping the Results of Geographically Weighted Regression

The Distribution of Environmental Equity: Exploring Spatial Nonstationarity in Multivariate Models of Air Toxic Releases