A Data Science Framework for Movement

Movement is the driving force behind the form and function of many ecological and human systems. Identification and analysis of movement patterns that may relate to the behavior of individuals and their interactions is a fundamental first step in understanding these systems. With advances in IoT and the ubiquity of smart connected sensors to collect movement and contextual data, we now have access to a wealth of geo‐enriched high‐resolution tracking data. These data promise new forms of knowledge and insight into movement of humans, animals, and goods, and hence can increase our understanding of complex spatiotemporal processes such as disease outbreak, urban mobility, migration, and human‐species interaction. To take advantage of the evolution in our data, we need a revolution in how we visualize, model, and analyze movement as a multidimensional process that involves space, time, and context. This paper introduces a data science paradigm with the aim of advancing research on movement.     

Crime Risk Maps: A Multivariate Spatial Analysis of Crime Data

In crime analyses, maps showing the degree of risk help police departments to make decisions on operational matters, such as where to patrol or how to deploy police officers. This study statistically models spatial crime data for multiple crime types in order to produce joint crime risk maps. To effectively model and map the spatial crime data, we consider two important characteristics of crime occurrences: the spatial dependence between sites, and the dependence between multiple crime types. We reflect both characteristics in the model simultaneously using a generalized multivariate conditional autoregressive model. As a real‐data application, we examine the number of incidents of vehicle theft, larceny, and burglary in 83 census tracts of San Francisco in 2010. Then, we employ a Bayesian approach using a Markov chain Monte Carlo method to estimate the model parameters. Based on the results, we detect the crime hotspots, thus demonstrating the advantage of using a multivariate spatial analysis for crime data.     

A SPATIAL CLIFF‐ORD‐TYPE MODEL WITH HETEROSKEDASTIC INNOVATIONS: SMALL AND LARGE SAMPLE RESULTS*

ABSTRACT In this paper, we specify a linear Cliff‐and‐Ord‐type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate multistep GMM/IV‐type estimation procedures for the parameters of the model. We also give the limiting distributions for our suggested estimators and consistent estimators for their asymptotic variance‐covariance matrices. We conduct a Monte Carlo study to show that the derived large‐sample distribution provides a good approximation to the actual small‐sample distribution of our estimators.     

Spatial Cluster Detection in Spatial Flow Data

As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single‐location spatial events or fail to preserve the integrity of spatial flow events. In this research we introduce a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K‐function, while maintaining the integrity of flow data. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgrades the classical hot spot detection method to the stage of “hot flow” detection. Several specific aspects of the method are discussed to provide evidence of its robustness and expandability, such as the multiscale issue and relative importance control, using a real data set of vehicle theft and recovery location pairs in Charlotte, NC.     

When Do We “Just Say No”? Policy Termination Decisions in Local Hospital Services

This paper develops a two‐stage decision‐making model of the public policy termination process, which integrates political and economic influences on local decision makers. We empirically explore the model using data on the provision of local public hospitals in California over 1981–95. The results provide support for the model as we find that triggering events as well as characteristics of the local decision‐making context affect the termination decision. For the case of public hospitals, we find that lower state and local revenue growth rates increase the likelihood of termination, while decision makers in communities with a larger local health budget, more unionized public employees, and a larger private hospital sector are less likely to terminate local public hospitals. The implications for public policy and for our understanding of the termination process are discussed.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

ART‐P‐MAP Neural Networks Modeling of Land‐Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land‐use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART‐Probability‐Map (ART‐P‐MAP), tailored to address these issues in the context of spatial modeling of land‐use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell‐level uncertainty measurement. Compared with two baselines, ART‐P‐MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land‐use change problem. Finally, we conclude that, as a general probabilistic regression model, ART‐P‐MAP is applicable to a broad range of land‐use change modeling approaches, which deserves future research.     

Raise your voices! Civilian protest in civil wars

Under what conditions do protests occur in civil wars? Evidence from case studies suggests that protests can indeed play an important role in contexts of civil wars, with civilians using respective tactics both against the state and rebels. We argue that localities experiencing armed clashes are likely to see protest events in the same month. Civilians conduct protests due to battle-related changes in the local opportunity structures and grievances related to losses experienced through collateral damage. Using spatially disaggregated data on protest and battle events in African civil wars, we find support for our hypothesis that battles trigger civilian protests. This effect is robust to the inclusion of a comprehensive list of confounding variables and alternative model specifications, including the use of different temporal and spatial units. Our findings highlight the role of the civilian population and the spatial relationship between war events and protests in civil wars.     

EXTENDING A SMOOTH PARAMETER MODEL TO FIRM LOCATION ANALYSES: THE CASE OF NATURAL GAS ESTABLISHMENTS IN THE UNITED STATES

This paper extends recent developments in regional growth modeling that use spatial regime switching functions to a count regression model of firm location events. The smooth parameter count model (SPCM) allows for a parsimonious parameterization of locally varying coefficients while simultaneously attending to excess‐zero count events. An empirical application examines natural gas establishment growth between 2005 and 2010. The smooth parameter model appears to outperform a standard zero‐inflated count model. The SPCM may be extended to the location analysis of other industries with the identification of transition variables related to the supply or demand oriented cost structure of the sector.     

Analyzing the Relationship Between Smoking and Coronary Heart Disease at the Small Area Level: A Bayesian Approach to Spatial Modeling

We model the relationship between coronary heart disease and smoking prevalence and deprivation at the small area level using the Poisson log-linear model with and without random effects. Extra-Poisson variability (overdispersion) is handled through the addition of spatially structured and unstructured random effects in a Bayesian framework. In addition, four different measures of smoking prevalence are assessed because the smoking data are obtained from a survey that resulted in quite large differences in the size of the sample across the census tracts. Two of the methods use Bayes adjustments of standardized smoking ratios (local and global adjustments), and one uses a nonparametric spatial averaging technique. A preferred model is identified based on the deviance information criterion. Both smoking and deprivation are found to be statistically significant risk factors, but the effect of the smoking variable is reduced once the confounding effects of deprivation are taken into account. Maps of the spatial variability in relative risk, and the importance of the underlying covariates and random effects terms, are produced. We also identify areas with excess relative risk.     

An Approach to Urban Dynamics

Spatial cycles that are general to the urbanization process have been widely observed in the developed countries. These include (a) the decline of large agglomerations and the emergence of medium-sized municipalities, and (b) population decreases in the core and population increases in the suburban parts of the metropolitan area, followed later by contrasting increases in the core and decreases in the suburbs. In this paper, we formulate a mathematical model for such spatial cycles of the urbanization process as those mentioned above. Based on this model, we ascertain some general characteristics of these cycles of urban dynamics. From our findings, we can propose a long-term strategy for the urbanization process.     

A Wavelet‐Based Extension of Generalized Linear Models to Remove the Effect of Spatial Autocorrelation. 基于小波扩展广义线性模型消除空间自相关的影响

Biogeographical studies are often based on a statistical analysis of data sampled in a spatial context. However, in many cases standard analyses such as regression models violate the assumption of independently and identically distributed errors. In this article, we show that the theory of wavelets provides a method to remove autocorrelation in generalized linear models (GLMs). Autocorrelation can be described by smooth wavelet coefficients at small scales. Therefore, data can be decomposed into uncorrelated and correlated parts. Using an appropriate linear transformation, we are able to extend GLMs to autocorrelated data. We illustrate our new method, called the wavelet‐revised model (WRM), by applying it to multiple regression with response variables conforming to various distributions. Results are presented for simulated data and real biogeographical data (species counts of the plant genus Utricularia [bladderworts] in grid cells throughout Germany). The results of our WRM are compared with those of GLMs and models based on generalized estimating equations. We recommend WRMs, especially as a method that allows for spatial nonstationarity. The technique developed for lattice data is applicable without any prior knowledge of the real autocorrelation structure.     

Semiparametric Maximum Likelihood Estimates of Spatial Dependence

We semiparametrically model spatial dependence via a combination of simpler weight matrices (termed spatial basis matrices) and fit this model via maximum likelihood. Estimation of the model relies on the intuition that bounds to the log‐determinant term in the log‐likelihood can provide penalties to overfitting both the level and pattern of spatial dependence. By relying on symmetric and doubly stochastic spatial basis matrices that reflect different weight specifications assigned to neighboring observations, we are able to derive a mathematical expression for bounds on the log‐determinant term that appears in the likelihood function. These bounds can be conveniently calculated allowing us to solve for maximum likelihood estimates at the bounds using a simple optimization over two quadratic forms that involve small matrices. An intuitively pleasing aspect of our approach is that the objective function for the bounded log‐likelihoods contains one quadratic form equal to the sum‐of‐squared errors measuring the quality of fit, and another quadratic form reflecting a penalty to overfitting spatial dependence. We apply our semiparametric estimation method to a housing model using 57,647 U.S. census tracts.     

BAYSIAN INFERENCE FOR ORDERED RESPONSE DATA WITH A DYNAMIC SPATIAL‐ORDERED PROBIT MODEL

ABSTRACT Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial‐ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It incorporates spatial effects in an ordered probit model by allowing for interregional spatial interactions and heteroskedasticity, along with random effects across regions or any clusters of observational units. The model assumes an autoregressive, AR(1), process across latent response values, thereby recognizing time‐series dynamics in panel data sets. The model code and estimation approach is tested on simulated data sets, in order to reproduce known parameter values and provide insights into estimation performance, yielding much more accurate estimates than standard, nonspatial techniques. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two‐dimensional autocorrelation.     

Socio‐spatial mix and inter‐ethnic attitudes: Jewish newcomers and Arab‐Jewish issues in the Galilee

Urban and regional planners tend to recommend spatial mix of socially diverse populations as an appropriate strategy to achieve social equity and improve inter‐group relations. However, the actual impact of such a mix on social relations in general, and inter‐ethnic attitudes in particular, has been subject to on‐going, yet inconclusive, debates among social scientists. This paper adds to the study of these issues by examining the inter‐ethnic attitudes of residents in Jewish ‘new settlements’ (elsewhere termed ‘community settlements’, or ‘mitzpim'), which were established some 15 years ago among the Arab villages of Israel's central Galilee region. We found that despite certain strands of ethnocentrism, most Jewish settlers hold significantly more moderate views on Arab‐Jewish issues than: (a) the general (non‐Galilee) Jewish public in Israel; and (b) the region's Arab population. The influence of the socio‐spatial mix on the moderation of hostile attitudes, at least among the Jews, is analyzed and explained by comparing our data with the findings of previous research on the topic. On the basis of that comparison we conclude that the Arab — Jewish mix in the Galilee, along with socio‐economic characteristics of the Jewish population and the existence of a ‘penetrating group phenomenon’, have combined to moderate Jewish attitudes in the study region. Planners are called upon to use this knowledge.     

Avoiding Blame—A Comprehensive Framework and the Australian Home Insulation Program Fiasco

It is widely acknowledged in the literature that the study of blame avoidance behavior (BAB) exhibited by public officials is scattered and unconcentrated, and that, for the most part, it neglects both contextual factors and comparative research. These deficits inhibit the production of the kind of generalized findings necessary to better understand potential consequences for the policy process and the workings of political systems. We address these deficits by developing a framework that takes stock of blame avoidance research, clarifies the explanatory potential of contextual factors, and allows for a systematic context‐sensitive cross‐case analysis. For illustrative purposes, the framework is applied to the Home Insulation Program in Australia as a critical case. This case reveals the explanatory potential of contextual factors for the understanding of BAB and the consequences thereof. We conclude by stating the advantages of our framework and explain how it can be used for comparative research.     

Rural business programs and business performance: The impact of the USDA's Business and Industry (B&I) Guaranteed Loan Program

The USDA's Rural Development (RD) has implemented several loan and grant programs to support businesses located in rural areas and the Business and Industry (B&I) Guaranteed Loan Program is the largest among them. The focus of the present study is to estimate the impact of B&I program participation and the amount of loans received on the probability of survival and employment growth of recipient businesses over the period of 1990–2013. Using administrative data on B&I loans from RD and business‐level data from the National Establishment Time‐Series (NETS), we utilize a matched difference‐in‐difference estimation strategy to assess the impact of the B&I program on establishment‐level outcomes. We find that the receipt of a B&I loan helps recipient businesses to reduce the risk of failure and grow slightly faster, compared with a similar control group of businesses. We also test if the impacts vary with the loan size and find that while increasing the size of loans has an increasing effect to reduce the risk of failure, it shows no statistically significant effect on employment growth.     

Shifting narrative strategies: How monument advocates change their stories in response to conflict over time

This paper expands the Narrative Policy Framework (NPF) by employing an exploratory case study approach to examine the construction of narratives temporally. A large-N Twitter dataset concerning the Bears Ears and Grand Staircase-Escalante monuments controversy is utilized to examine the question: how does the use of narrative strategies change over time? Through the application of change-point analysis, we determine time points of significant shifts towards use of the devil-angel shift, scope of the conflict, and causal mechanism strategies. Overall, we find that organizations do not vary their use of narrative strategies over the course of a policy conflict but instead demonstrate discrete changes in response to certain policy events. Based on our findings, we conclude with suggestions for refining and expanding NPF hypotheses. Specifically, we recommend a more contextual analysis of shifts in narrative strategy use in response to specific events over time.     

UNDER DIVINE WINGS: FORMS OF IDEOLOGICAL AND TERRITORIAL DOMINATION DURING THE IBERIAN IRON AGE

This article explores the ways in which the Iberian communities of the Iron Age developed a model of extension and legitimization for their social hierarchies. By analysing the testimonies of the ideational realm and the territorial occupation of the Iberian populations, it is argued that the representation of a winged goddess was used by certain families to legitimize the control and possession of natural resources. Thus, the contextual analysis of this goddess can explain a territorial domination established in the southern sub‐plateau of the Iberian Peninsula. A Mediterranean model of the goddess is transformed by combining traditional and foreign elements to create a unique synthesis. What draws our attention, though, is how this new being was eventually integrated into the changes that took place in local populations, which established new constructions of space and new relationships of patronage. New practices appear, such as the persistence of ancient forms of pottery and a symbolic opposition to imported objects. In the following pages, I will identify the underlying process as a territorial division conducted by certain settlements as they explored a broader spatial control. I will explore one of these territories and the ideology employed to implement this form of domination.     

Statistical Network Analysis for Analyzing Policy Networks

To analyze social network data using standard statistical approaches is to risk incorrect inference. The dependencies among observations implied in a network conceptualization undermine standard assumptions of the usual general linear models. One of the most quickly expanding areas of social and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs), and stochastic actor‐oriented models. We focus most attention on ERGMs by providing an illustrative example of a model for a strategic information network within a local government. We draw inferences about the structural role played by individuals recognized as key innovators and conclude that such an approach has much to offer in analyzing the policy process.