Computation of the distance between a polygon and a point in spatial analysis

Distance is an important and basic concept in geography. Many theories, methods, and applications involve distance explicitly or implicitly. While measuring the distance between two locations is a straightforward task, many geographical processes involve areal units, where the distance measurement can be complicated. This research investigates distance measurement between a location (point) and an area (polygon). We find that traditional polygon‐to‐point distance measurements, which involve abstracting a polygon into a central or representative point, could be problematic and may lead to biased estimates in regression analysis. To solve this issue, we propose a new polygon‐to‐point distance metric along with two algorithms to compute the new distance metric. Simulation analysis shows the effectiveness of the new distance metric in providing unbiased estimates in linear regression.

How (Not) to Lie with Spatial Statistics

Recently, random forest (RF) as a highly flexible machine learning algorithm has been applied to medicine, biology, machine learning, computer vision and other fields, and has shown good application performance. Nevertheless, the operation efficiency and identification accuracy of RF algorithm are actually affected by the number of the decision trees. A novel RF model, referred to as the extreme random forest (ERF), was proposed to improve the ability of feature extraction and reduce the computation burden. In the ERF method, the dimensionality of the high-dimensional data is randomly reduced through the random mapping matrix, and the classification performance after dimensionality reduction is improved. In this way, the sample dimension of the input RF is greatly reduced, which improves the operation efficiency of the RF. Both theoretical analysis and experiment tests have verified the superiority of the proposed method. In the experimental part, the present ERF method was compared with other peer method in terms of diagnostic performance and computational efficiency. The comparison results showed that the ERF method has more advantages both in diagnostic accuracy and computational efficiency. In addition to mechanical fault diagnosis, the proposed ERF can also be used in other machine learning fields.

We give new and efficient black-box reconstruction algorithms for some classes of depth-3 arithmetic circuits. As a consequence, we obtain the first efficient algorithm for computing the tensor rank and for finding the optimal tensor decomposition as a sum of rank-one tensors when then input is a constant-rank tensor. More specifically, we provide efficient learning algorithms that run in randomized polynomial time over general fields and in deterministic polynomial time over and for the following classes: 1) Set-multilinear depth-3 circuits of constant top fan-in ((k) circuits). As a consequence of our algorithm, we obtain the first polynomial time algorithm for tensor rank computation and optimal tensor decomposition of constant-rank tensors. This result holds for d dimensional tensors for any d, but is interesting even for d=3. 2) Sums of powers of constantly many linear forms ((k) circuits). As a consequence we obtain the first polynomial-time algorithm for tensor rank computation and optimal tensor decomposition of constant-rank symmetric tensors. 3) Multilinear depth-3 circuits of constant top fan-in (multilinear (k) circuits). Our algorithm works over all fields of characteristic 0 or large enough characteristic. Prior to our work the only efficient algorithms known were over polynomially-sized finite fields (see. Karnin-Shpilka 09’). Prior to our work, the only polynomial-time or even subexponential-time algorithms known (deterministic or randomized) for subclasses of (k) circuits that also work over large/infinite fields were for the setting when the top fan-in k is at most 2 (see Sinha 16’ and Sinha 20’).

Manuscript title: Geospatial analysis of Cancer risk and residential proximity to coal mines in Illinois

Human has the ability to roughly estimate the distance of objects because of the stereo vision of human’s eyes. In this paper we proposed an improved stereo vision system to accurately measure the distance of objects in real world. Object distance is very useful for obstacle avoidance and navigation of autonomous vehicles. Recent researches have used stereo cameras for different applications such as 3D image construction, distance measurement, and occlusion detection. The proposed measurement procedure is a three-phase process: object detection, segmentation, and distance calculation. In distance calculation, we proposed a new algorithm to reduce the error. The result shows our measurement system is capable of providing objects distance with less than 5% of measurement error.

Heuristic Methods for Location-Allocation Problems

BackgroundHaiti has been experiencing a resurgence of diphtheria since December 2014. Little is known about the factors contributing to the spread and persistence of the disease in the country. Geographic information systems (GIS) and spatial analysis were used to characterize the epidemiology of diphtheria in Haiti between December 2014 and June 2021.MethodsData for the study were collected from official and open-source databases. Choropleth maps were developed to understand spatial trends of diphtheria incidence in Haiti at the commune level, the third administrative division of the country. Spatial autocorrelation was assessed using the global Moran’s I. Local indicators of spatial association (LISA) were employed to detect areas with spatial dependence. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were built to identify factors associated with diphtheria incidence. The performance and fit of the models were compared using the adjusted r-squared (R2) and the corrected Akaike information criterion (AICc).ResultsFrom December 2014 to June 2021, the average annual incidence of confirmed diphtheria was 0.39 cases per 100,000 (range of annual incidence = 0.04–0.74 per 100,000). During the study period, diphtheria incidence presented weak but significant spatial autocorrelation (I = 0.18, p<0.001). Although diphtheria cases occurred throughout Haiti, nine communes were classified as disease hotspots. In the regression analyses, diphtheria incidence was positively associated with health facility density (number of facilities per 100,000 population) and degree of urbanization (proportion of urban population). Incidence was negatively associated with female literacy. The GWR model considerably improved model performance and fit compared to the OLS model, as indicated by the higher adjusted R2 value (0.28 v 0.15) and lower AICc score (261.97 v 267.13).ConclusionThis study demonstrates that GIS and spatial analysis can support the investigation of epidemiological patterns. Furthermore, it shows that diphtheria incidence exhibited spatial variability in Haiti. The disease hotspots and potential risk factors identified in this analysis could provide a basis for future public health interventions aimed at preventing and controlling diphtheria transmission.

We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we re- examine earlier published results (Friedman and Lichter (Popul Res Policy Rev 17:91-109, 1998)). We find that formal tests for spatial autocorrelation among county child poverty rates confirm and quantify what is obvious from simple maps of such rates: the risk of a child living in poverty is not (spatially) a randomly distributed risk at the county level. Explicit acknowledgment of spatial effects in an explanatory regression model improves considerably the earlier published regression results, which did not take account of spatial autocorrelation. These improvements include: (1) the shifting of ''wrong sign'' parameters in the direction originally hypothesized by the authors, (2) a reduction of residual squared error, and (3) the elimination of any substantive residual spatial autocorrelation. While not without its own problems and some remaining ambiguities, this reanalysis is a convincing demonstration of the need for demographers and other social scientists to examine spatial autocorrelation in their data and to explicitly correct for spatial externalities, if indicated, when performing multiple regression analyses on variables that are spatially referenced. Substantively, the analysis improves the estimates of the joint effects of place- influences and family-influences on child poverty.

Stored grain insect species in bulk-stored barley were sampled during 15 consecutive weeks in two ways: direct sampling based on grain trier samples and indirect sampling based on probe trap captures. A total number of 22 insect taxa were found; this study focused on the six most abundant species and their natural enemies. Four aspects were addressed: (i) differences in insect counts when based on either probe trap captures or grain trier samples, (ii) usefulness of grain temperature and moisture content as explanatory variables for insect densities, (iii) density-dependent relationships between natural enemies and their hosts, and (iv) spatial and non-spatial analyses of insect counts. Both total captures and frequencies of insect taxa were consistently higher in captures with probe traps than insect numbers obtained from grain samples. Regression analysis with abiotic conditions and probe trap captures as explanatory variables provided good fits to insect counts in grain samples for four of the six insect species (R2-values>0.40). Using multi-regression analyses, we showed that: (i) the occurrence of natural enemies was only weakly associated with the abundance of hosts; (ii) grain moisture content and temperature appeared to be at least as important variables as host availability; and (iii) the predictive strengths of regression models were similar when based on either data from grain samples or probe traps. Spatial analyses (SADIE) of both sampling data suggested that all data sets followed a spatially random distribution; re-arrangement of the data provided insight into important aspects of SADIE analyses of small data sets. Non-spatial analysis (Lloyd's aggregation index) showed significant differences among species and that the level of non-spatial aggregation was quite sensitive to sampling method used.

Background – Spatial analysis technology can provide a better understanding of dengue virus transmission dynamics, thus allowing for the improvement of prevention and control of the disease. Objectives – To describe the occurrence of dengue fever in the city of Varzea Paulista, Southeastern Brazil, between 1998 and 2012 and to characterize the epidemic occurred in 2007; to identify, based on the epidemic’s data, the spatial and spatio temporal distribution of the risk of dengue; evaluating, based on the occurrence of the disease, the relationship between incidence rates and socioeconomic, demographic and environmental variables, taking as units of analysis, the census tracts. Methods – Data were obtained from Sistema de Informacao de Agravos de Notificacao (SINAN – Information System for Notifiable Diseases). Dengue fever cases registered in the city were geocoded by street names and grouped according to 165 census tracts, thus generating thematic maps. Incidence rates were calculated for the study period, as well as the identification of higher and lower-risk areas for space and space-time clusters of dengue fever. Geocoding, spatial analysis and generation of maps were performed using the softwares ArcGis, TerraView, and SatScan. Spatial relative risk was obtained from a generalized additive model for a case-control study. The association between socioeconomic variables and incidence rates using spatial regression analysis was tested in order to find the best explanatory model for the dengue fever epidemic in 2007. Results – The maps generated showed the spatial and space-time distribution of dengue fever in the city, the higher risk areas, and the course of the epidemic during the epidemiological weeks. Two clusters were identified in 2007. The high risk cluster was related to poor sanitary conditions whereas individuals in the low risk cluster showed the best socioeconomic indicators. Spatial, space-time and regression analyses provided similar results regarding the higher incidence and risk in areas with the worst socioeconomic indicators. Conclusions – The first dengue fever epidemic in the city can be explained by the immunological status of the population when a new serotype was introduced and the conditions of social vulnerability. The method has proven efficient for identification of risk areas, thus allowing for better planning and resource allocation from the government. Descriptors: Dengue; Geographic Information Systems; Spatial Analysis; Epidemiological Surveillance; Ecological Studies

This special issue consists of a collection of articles that describe innovations in spatio-temporal methodology. Spatio-temporal statistics has been developing at a rapid pace over the past 25 years. The topic is briefly covered in Cressie's (1993) comprehensive book on spatial statistics. Subsequently, Cressie and Wikle (2011) provided the first comprehensive book on spatio-temporal statistics, but it focused primarily on linear, univariate, and Gaussian methods. More recently, there have been numerous significant advancements in non-linear, multivariate, and non-Gaussian spatio-temporal methods, particularly those suited for ‘big’ data problems. Yet, each of these topic areas is relatively underdeveloped, and there are still many research challenges. Historically, spatio-temporal statistics methodology has developed more as an extension of spatial statistics rather than time series. That is, the emphasis has been on specification of models through their second-order structure, typically within a Gaussian process framework. Alternatively, dynamic spatio-temporal models (DSTMs) have been used to model processes for which it is more realistic to think of spatial processes evolving through time, that is, when it is more reasonable to think of the process conditionally (in time) rather than marginally (as with the Gaussian process framework). DSTMs often have model forms as in classical multivariate time series models, but they present unique challenges in that the types of relationships between space and time are often driven by mechanistic processes, and the associated statistical models must attempt to accommodate these relationships. In addition, the dimensionality of the spatial components of these models often prohibits the use of classical multivariate time series methods. Although much of the development of spatio-temporal methodology has been driven by environmental, epidemiological, and ecological applications (e.g. pollution monitoring, weather forecasting, climate, oceanography, disease mapping, invasive species, animal movement, etc.), there is an increasing number of novel methodologies that are motivated by the biological sciences (e.g. brain science), federal statistics (e.g. multivariate surveys, non-Gaussian change of support), sociological statistics (e.g. crime analysis), and econometric (e.g. multivariate panel) applications. Indeed, these processes are often multivariate, non-linear, and/or non-Gaussian. The articles in this special issue provide a representative snapshot of innovative work in these areas. Specifically, the articles selected for this issue highlight non-Gaussian spatio-temporal models, computational efficiency, and/or improving parameterizations of DSTMs. In their article ‘Scalable Inference for Space-Time Gaussian Cox Processes’, Shirota and Banerjee show how one can use an efficient data augmentation approach in conjunction with nearest-neighbor log-Gaussian processes to efficiently model count (crime event) data via spatio-temporal Gaussian Cox processes. In ‘Estimating Spatial Changes Over Time of Artic Sea Ice Using Hidden 2 × 2 Tables’, Zhang and Cressie consider non-Gaussian (Bernoulli) data on the presence/absence of sea ice. The model is cast in an efficient manner by using dimension-reduced spatio-temporal processes in an EM algorithm, cleverly describing the process as time-varying 2 × 2 tables. Tagle, Castruccio, Crippa, and Genton, in their article ‘A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multivariate Skew-T Distribution’, show how to build an efficient and realistic ‘weather generator’ for daily wind speeds that is able to simulate the type of skewed distributions that are often found in weather variables. In their article ‘On a Semiparametric Data-Driven Nonlinear Model with Penalized Spatio-Temporal Lag Interactions’, Al-Sulami, Jiang, Lu, and Zhu consider a non-linear (semiparametric) regression spatio-temporal model that also includes a dynamic interaction term and consider an adaptive lasso approach for selecting the space–time lag interactions that are most useful (in their example, to model US housing price data). Their methodology does not rely on a Gaussian error assumption. In the spirit of efficient modeling of the spatio-temporal terms beyond a trend and seasonality, Gao and Tsay present an efficient structured factor approach for multivariate time series and spatio-temporal data (e.g. PM2.5 pollutant observations) in their article ‘A Structural-Factor Approach to Modeling High-Dimensional Time Series and Space-Time Data’. Finally, in their article ‘Spatio-Temporal Models for Big Multinomial Data Using the Conditional Multivariate Logit-Beta Distribution’, Bradley, Wikle and Holan develop a novel efficient conjugate Bayesian algorithm to model high-dimensional spatio-temporal multinomial data, which includes a spatio-temporal dynamic process. They apply this model to public-use Quarterly Workforce Indicators from the Longitudinal Employer Household Dynamics program of the US Census Bureau. These articles provide excellent examples of how spatio-temporal statistical models can be applied to address complex non-Gaussian, non-linear, and multivariate data with efficient computational algorithms. These problems are by no means ‘solved’, but it is our hope that the readers of the Journal of Time Series Analysis may see connections to their own work that can be applied to this rich set of problems and this growing and important area of statistics. Finally, we would like to end this Editorial by thanking Prof. A. M. R. Taylor for encouraging us to edit this special issue.

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey

In spatial statistics, spatial regression methods are often used to quantify the relative influence of factors on health and crime, among others. Spatial Lag Model (SLM) and Spatial Error Model (SEM) are widely adopted in spatial regression analysis. However, these models assume that dependent variables are continuous and normally distributed and require that parameters must be non-random variables. These assumptions limit the processing or analysis of some spatial information systematically. As opposed to this, a Bayesian spatial regression model treats data as fixed and unknown quantities or parameters as random variables expressed in terms of probabilities. Thus, it can leverage information on the adjacent regions to estimate the dependent variables, overcoming the data sparseness and small-area problem that spatial analysis often encounters. This approach also makes the estimation of model parameters more stable. In spatial statistics, spatial regression methods are often used to quantify the relative influence of factors on health and crime, among others. Spatial Lag Model (SLM) and Spatial Error Model (SEM) are widely adopted in spatial regression analysis. However, these models assume that dependent variables are continuous and normally distributed and require that parameters be non-random variables. These assumptions limit the processing or analysis of some spatial information systematically. As opposed to this, a Bayesian spatial regression model treats data as fixed and unknown quantities or parameters as random variables expressed in terms of probabilities. Thus, it can leverage information on the adjacent regions to estimate the dependent variables, overcoming the data sparseness and small-area problem that spatial analysis often encounters. This approach also makes the estimation of model parameters more stable.

GIScience 2016 Short Paper Proceedings Searching for Common Ground (Again) 1 J. Thatcher​ 1​ , L. Bergmann​ 2​ , D. O’Sullivan​ 3 University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA 98402 Email: jethatch@uw.edu University of Washington, Box 353550, Seattle, WA 98195 Email: luke.bergmann@gmail.com University of California Berkeley, 504 McCone Hall #4740, Berkeley, CA 94720 Email: dosullivan@berkeley.edu Abstract At over twenty­five years old, GIScience has been successful academically and institutionally. However, its relationship to one of its ‘natural’ homes, the discipline of Geography, has often been troubled and uncertain. We suggest that from the founding of GIScience, its close association with Geographical Information Systems (GIS) has contributed to an acceptance of an absolute coordinate space ​ ​ encoded ​ ​ as (​ x , y [, ​ z , ​ t ]) as both a relatively unproblematic and dominant representation of geographical space. We briefly consider how this situation may have arisen, perhaps as an unintended consequence of an originally tactical disciplinary positioning move. However, our purpose here is ​ not criticism, but to highlight the many other more minor strands within GIScience, which can provide fertile common ground for renewed conversations between GIScience and Geography. We suggest congruences between less dominant strands of research in GIScience and theoretical concepts in Geography, as an invitation to constructive collaborations. 1. Introduction “GIScience,” as a term, is around a quarter of a century old. It has become institutionally and academically valorized through multiple conferences, journals, and textbooks. We argue that from its inception as distinct from GISystems (GIS), GIScience (GISci) has been framed in ways that have partially delimited the spatial thinking that occurs within it. Specifically, by framing GISci as fundamentally concerned with “spatial information” and “spatial analysis” of such information (Goodchild 1992, 2006), GISci has tended to accept space as absolute, transforming it into data whose ‘atomic’ units are measurements within a coordinate system of the form (​ x , ​ y [, ​ z , ​ t , etc.]) (Goodchild et al. 2007), and de­emphasizing alternative conceptualizations of space, time, and process. While this reflects GISci’s construction as a “science ​ of ​ geographical information ” (Goodchild 1992: 38, emphasis ours), we suggest that, in taking a specific concept of spatial information as given, GISci may have unnecessarily distanced itself from broader research in computational geography and in geographic thought. To make this argument, first, we briefly examine how GISci was framed in an important early conceptualization, focusing on definitions of geographic information. Next, we provide evidence suggestive of continuing ties between these early framings and ongoing research in the field. Finally, we suggest that a reconsideration of the conceptualizations of both space and spatial processes found in broader computational and quantitative geography as well as some theoretical realms of (human) Geography could be valuable to GISci. Our title is a nod to ​ A Search for Common Ground (Gould and Olsson 1982) and the follow up ​ A Ground for Common Search (Golledge et al. 1986) which were among the last attempts to bridge the philosophical divides between quantitative and ‘critical’ geography before the 1990s ‘science wars’ and, more recently, ‘critical GIS’.