Spatial Analysis

Sociology ◽  
2011 ◽  
Author(s):  
Stephen Matthews ◽  
Rachel Bacon ◽  
R. L’Heureux Lewis-McCoy ◽  
Ellis Logan
Keyword(s):  
Spatial Analysis ◽  
Spatial Statistics ◽  
Spatial Econometrics ◽  
Spatial Data ◽  
Spatial Inequality ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Sociological Research ◽  
Data Types ◽  
Wide Range

Recent years have seen a rapid growth in interest in the addition of a spatial perspective, especially in the social and health sciences, and in part this growth has been driven by the ready availability of georeferenced or geospatial data, and the tools to analyze them: geographic information science (GIS), spatial analysis, and spatial statistics. Indeed, research on race/ethnic segregation and other forms of social stratification as well as research on human health and behavior problems, such as obesity, mental health, risk-taking behaviors, and crime, depend on the collection and analysis of individual- and contextual-level (geographic area) data across a wide range of spatial and temporal scales. Given all of these considerations, researchers are continuously developing new ways to harness and analyze geo-referenced data. Indeed, a prerequisite for spatial analysis is the availability of information on locations (i.e., places) and the attributes of those locations (e.g., poverty rates, educational attainment, religious participation, or disease prevalence). This Oxford Bibliographies article has two main parts. First, following a general overview of spatial concepts and spatial thinking in sociology, we introduce the field of spatial analysis focusing on easily available textbooks (introductory, handbooks, and advanced), journals, data, and online instructional resources. The second half of this article provides an explicit focus on spatial approaches within specific areas of sociological inquiry, including crime, demography, education, health, inequality, and religion. This section is not meant to be exhaustive but rather to indicate how some concepts, measures, data, and methods have been used by sociologists, criminologists, and demographers during their research. Throughout all sections we have attempted to introduce classic articles as well as contemporary studies. Spatial analysis is a general term to describe an array of statistical techniques that utilize locational information to better understand the pattern of observed attribute values and the processes that generated the observed pattern. The best-known early example of spatial analysis is John Snow’s 1854 cholera map of London, but the origins of spatial analysis can be traced back to France during the 1820s and 1830s and the period of morale statistique, specifically the work of Guerry, d’Angeville, Duplin, and Quetelet. The foundation for current spatial statistical analysis practice is built on methodological development in both statistics and ecology during the 1950s and quantitative geography during the 1960s and 1970s and it is a field that has been greatly enhanced by improvements in computer and information technologies relevant to the collection, and visualization and analysis of geographic or geospatial data. In the early 21st century, four main methodological approaches to spatial analysis can be identified in the literature: exploratory spatial data analysis (ESDA), spatial statistics, spatial econometrics, and geostatistics. The diversity of spatial-analytical methods available to researchers is wide and growing, which is also a function of the different types of analytical units and data types used in formal spatial analysis—specifically, point data (e.g., crime events, disease cases), line data (e.g., networks, routes), spatial continuous or field data (e.g., accessibility surfaces), and area or lattice data (e.g., unemployment and mortality rates). Applications of geospatial data and/or spatial analysis are increasingly found in sociological research, especially in studies of spatial inequality, residential segregation, demography, education, religion, neighborhoods and health, and criminology.

scholarly journals Spatial Data Analysis in epidemiology of sensorineural hearing loss

2020 ◽  
Vol 19 (4) ◽  
pp. 13-20
Author(s):  
A. A. Korneenkov ◽  
◽  
S. V. Ryazantsev ◽  
I. V. Fanta ◽  
E. E. Vyazemskaya ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Information Systems ◽  
Spatial Analysis ◽  
Sensorineural Hearing Loss ◽  
Spatial Data ◽  
Sensorineural Hearing ◽  
Spatial Data Analysis ◽  
Point Pattern ◽  
Spatial Process ◽  
One Year

The identification of risk factors, features and patterns of the emergence and spread of diseases in space requires a large array of diverse data and the use of a serious mathematical and statistical apparatus. The distribution of diseases in space is studied using spatial analysis tools, which are now widely used as information systems are introduced and data are accumulated that are relevant to public health. For most tasks of working with spatial data (data, events that have geographical, spatial coordinates), various geographic information systems are used. As a disease for spatial analysis, sensorineural hearing loss was chosen, with which patients were treated at the Saint-Petersburg Research of Ear, Throat, Nose and Speech during one year of the study. The main tasks of the spatial analysis of data on the incidence of sensorineural hearing loss (SNHL) for hospitalization were: visualization of a point pattern, which can form the geographical coordinates of the places of residence of inpatients with SNHL; assessment of the properties of the spatial process that generates this point image (assessment of the intensity of the process, its laws) using various statistical indicators; testing the hypothesis about the spatial randomness of this process and the influence of individual factors on it. R-code accompanied all calculations in the article. Calculations can be reproduced quite easily. The text of the article can be used as step-by-step instructions for their implementation.

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

Analyzing Resilience in Local Labor Market Areas: A Spatial Analysis for the Case of Italy

2019 ◽  
Vol 65 (3) ◽  
pp. 189-208
Author(s):  
Barbara Martini ◽  
Marco Platania
Keyword(s):  
Labor Market ◽  
Spatial Analysis ◽  
Spatial Data ◽  
Spatial Relationship ◽  
Spatial Data Analysis ◽  
Market Area ◽  
Observation Unit ◽  
Local Labor Market ◽  
Market Areas ◽  
The Relationship

Abstract The aim of the paper is to analyse if and in which way specialization, geographical localization and spill-over effects affect resilience. The research is carried out using LLMAs (Local Labor Market Areas) as observational unit and spatial data analysis techniques (Anselin 1999, LeSage & Pace, 2009) in Italy. Resilience literature focalized its attention on regions. Despite this, there is no general agreement regarding the most appropriate observation unit. Our aim is not only to investigate the relationship between specialization and resilience at smaller scale using the LLMAs as observation unit but also to explore the spatial relationship among them. Results highlight a strong spatial correlation among LLMAs. As consequence resilience is not only influenced by specialization but also by geographical localization through spill-over effects. JEL Classifications: R10, R12, C23, C33 Spatial analysis; Resilience; Labor Market Area; Italy

A Survey of Spatial Data Mining Methods Databases and Statistics Point of Views

2011 ◽  
pp. 229-242 ◽  
Author(s):  
Karine Zeitouni
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Mining Methods

This chapter reviews the data mining methods that are combined with Geographic Information Systems (GIS) for carrying out spatial analysis of geographic data. We will first look at data mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: the first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

Cognitive Aspects on the Representation of Dynamic Environmental Phenomena Using Animations

2001 ◽  
pp. 13-24 ◽  
Author(s):  
Patrick J. Ogao ◽  
Connie A. Blok
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Problem Solving ◽  
Spatial Data ◽  
Scientific Data ◽  
Data Availability ◽  
Data Exploration ◽  
Spatial Data Analysis ◽  
Wide Range ◽  
Interdisciplinary Nature

Measurements from dynamic environmental phenomena have resulted in the acquisition and generation of an enormous amount of data. This upsurge in data availability can be attributed to the interdisciplinary nature of environmental problem solving and the wide range of acquisition technology involved. In essence, users are dealing with data that is complex in nature, multidimensional and probably of a temporal nature. Also, the frequency by which this data is acquired far exceeds the rate at which it is being explored, a factor that has accelerated the search for innovative approaches and tools in spatial data analysis. These attempts have seen both analytical and visual techniques being used as aids in presentation and scientific data exploration. Examples are seen in techniques as in: data mining, data exploration and visualization.

scholarly journals Automated Conflation of Digital Elevation Model with Reference Hydrographic Lines

2020 ◽  
Vol 9 (5) ◽  
pp. 334
Author(s):  
Timofey E. Samsonov
Keyword(s):  
Spatial Analysis ◽  
Digital Elevation Model ◽  
Spatial Data ◽  
Drainage Network ◽  
Map Generalization ◽  
Data Types ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Different Sources

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

scholarly journals Spatial data analysis and the use of maps in scientific health articles

2016 ◽  
Vol 62 (4) ◽  
pp. 336-341
Author(s):  
Luciana Bertoldi Nucci ◽  
Patrick Theodore Souccar ◽  
Silvia Diez Castilho
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Geographical Location ◽  
Epidemiological Studies ◽  
Spatial Data Analysis ◽  
Careful Evaluation ◽  
Analysis Techniques ◽  
Impact Index ◽  
Scientific Papers ◽  
The Impact

Summary Introduction: Despite the growing number of studies with a characteristic element of spatial analysis, the application of the techniques is not always clear and its continuity in epidemiological studies requires careful evaluation. Objective: To verify the spread and use of those processes in national and international scientific papers. Method: An assessment was made of periodicals according to the impact index. Among 8,281 journals surveyed, four national and four international were selected, of which 1,274 articles were analyzed regarding the presence or absence of spatial analysis techniques. Results: Just over 10% of articles published in 2011 in high impact journals, both national and international, showed some element of geographical location. Conclusion: Although these percentages vary greatly from one journal to another, denoting different publication profiles, we consider this percentage as an indication that location variables have become an important factor in studies of health.

Spatial Statistics

2018 ◽  
Author(s):  
Christopher K. Wikle
Keyword(s):  
Machine Learning ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Point Processes ◽  
Hierarchical Modeling ◽  
Climate Science ◽  
Spatial Process ◽  
Wide Range ◽  
Spatio Temporal ◽  
Specific Implementation

The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.

scholarly journals Grazing trajectory statistics and visualization platform based on cloud GIS

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Dong Li ◽  
Chuanjian Wang ◽  
Qilei Wang ◽  
Tianying Yan ◽  
Wanlong Bing ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Feed Intake ◽  
Spatial Data ◽  
Walking Speed ◽  
Relevant Information ◽  
Spatial Data Analysis ◽  
Related Field ◽  
Line Chart ◽  
Point Analysis ◽  
Field Information

Abstract It is very important for ranchers and grassland livestock management departments to master the information on the trajectory and feeding behavior of the herd timely and accurately. Therefore, this study developed a statistics and visualization platform for grazing trajectory. The platform was implemented by using the Web AppBuilder for ArcGIS framework and ArcGIS Online server. In particular, the trajectory processing service on the server was used to calculate walking speed, walking trajectory and feed intake of the herd in the platform. And these results were published to the ArcGIS Online server. The relevant information was analyzed and displayed by Web AppBuilder for ArcGIS calling the data on ArcGIS Online. Moreover, the paltform provided some visualization functions to support the visualization of user-defined analysis results. When users use the functions of spatial analysis (such as buffer analysis, finding hot pots analysis and interpolation point analysis), they can choose to analyze spatial data and related field information to conduct customized spatial data analysis. In a short, the platform realized the visualization functions of feed intake statistics, walking speed statistics, spatial analysis, line chart analysis and pie chart analysis of spatial data related attributes. It can provide technical support and data support for the relevant management departments to monitor grazing information and study the living habits of the herd.

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