Spatial Data Analysis

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GeoDa: An Introduction to Spatial Data Analysis

GeoDa: An Introduction to Spatial Data Analysis

(1989) and cited as a major impediment to the adoption and use of spatial statistics by GIS researchers. Initially, attention tended to focus on conceptual issues, such as how to integrate spatial statistical methods and a GIS environ- ment (loosely vs. tightly coupled, embedded vs. modular, etc.), and which techniques would be most fruitfully included in such a framework. Familiar re- views of these issues are represented in, among others, Anselin and Getis (1992), Goodchild et al. (1992), Fischer and Nijkamp (1993), Fotheringham and Roger- son (1993, 1994), Fischer et al. (1996), and Fischer and Getis (1997). Today, the situation is quite different, and a fairly substantial collection of spatial data analysis software is readily available, ranging from niche programs, customized scripts and extensions for commercial statistical and GIS packages, to a bur- geoning open source effort using software environments such as R, Java and Python. This is exemplified by the growing contents of the software tools clear- ing house maintained by the U.S.-based Center for Spatially Integrated Social Science (CSISS). 1
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GeoDa: An Introduction to Spatial Data Analysis

GeoDa: An Introduction to Spatial Data Analysis

methods for dynamic graphics outlined in Cleveland and McGill (1988). In geographical analysis, the concept of ‘‘geographic brushing’’ was introduced by Monmonier (1989) and made operational in the Spider/Regard toolboxes of Haslett, Unwin, and associates (Haslett, Wills, and Unwin 1990; Unwin 1994). Several modern toolkits for exploratory spatial data analysis (ESDA) also incorporate dynamic linking, and, to a lesser extent, brushing. Some of these rely on interaction with a GIS for the map component, such as the linked frameworks combining XGobi or XploRe with ArcView (Cook et al. 1996, 1997; Symanzik et al. 2000); the SAGE toolbox, which uses ArcInfo (Wise, Haining, and Ma 2001); and the DynESDA extension for ArcView (Anselin 2000), GeoDa’s immediate predecessor. Linking in these implementations is constrained by the architecture of the GIS, which limits the linking process to a single map (in GeoDa, there is no limit on the number of linked maps). In this respect, GeoDa is similar to other freestanding modern implementations of ESDA, such as the cartographic data visualizer, or cdv (Dykes 1997), GeoVISTA Studio (Takatsuka and Gahegan 2002), and STARS (Rey and Janikas 2006). These all include functionality for dynamic linking, and to a lesser extent, brushing. They are built in open-source programming environments, such as Tkl/Tk (cdv), Java (GeoVISTA Studio), or Python (STARS) and thus easily extensible and customizable. In contrast, GeoDa is (still) a closed box, but of these packages it provides the most extensive and flexible form of dynamic linking and brushing for both graphs and maps.
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Current practices in cancer spatial data analysis: a call for guidance

Current practices in cancer spatial data analysis: a call for guidance

Of increasing concern in this field is protecting the privacy and confidentiality of the study subjects. While all researchers agree that this is important, it is often difficult to reconcile these needs with data needs for a proper anal- ysis. In particular, spatial data analysis and mapping of results are often hampered by the lack of specific addresses. Data collection agencies and medical facilities are imposing increasingly strict requirements for data release and often only identify a place (usually patient's address) to a broad administrative unit. For example, the recently enacted Health Insurance Portability and Accountability Act (HIPAA) often requires removal of geographic subdivisions smaller than the state, and the National Center for Health Statistics only releases death certificate data aggregated to the county level or for places with large populations. The reportable specificity of loca- tion is often not good enough to allow the analysis to answer research questions about the spatial patterns of the disease. Methods are currently being explored that would allow use of specific individual information in the analy- sis but would mask identifying characteristics in the results reported only at an aggregated level. In addition to such federal reporting restrictions, state and local govern- ments may add additional regulatory requirements. This said, such regulations need not prohibit spatial anal- ysis of health data completely, rather they change the con- text within which such analyses may occur. For instance, mutually agreeable memoranda of understanding
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Geography and economic performance: exploratory spatial data analysis for Great Britain

Geography and economic performance: exploratory spatial data analysis for Great Britain

The visualisation and exploration of spatial data can provide valuable insights into the nature and extent of spatial clustering in economic variables (Dall’Erba, 2005; Lopez-Bazo et al. 1999). However, much of the empirical work undertaken to date has tended to focus on identifying the spatial properties of a single economic variable – usually GDP per capita or its growth rate (see for example, Rey and Montouri, 1999; Ertur and Le Gallo, 2003; Roberts, 2004). In this paper, we use the techniques of exploratory spatial data analysis to compare and contrast patterns of spatial association in related measures of economic performance. More specifically, we decompose sub- regional income per worker into a productivity component and an occupational composition component, and analyse the spatial structure of each of these variables. This approach offers valuable insights into the sources of spatial dependence and spatial heterogeneity in income per worker. This is very distinct from the information that may be gained using spatial regression methods which focus on identifying and estimating average effects across space. 2
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A Software System for Spatial Data Analysis and Modeling *

A Software System for Spatial Data Analysis and Modeling *

Furthermore, precision agriculture data are inherently distributed at multiple farms and cannot be localized on any one machine for a variety of practical reasons including physically dispersed data sets over many different geographic locations, security services and competitive reasons. With the growth of networks this is often seen in other domains. In such situations, it is advantageous to have a distributed data mining system that can learn from large databases located at multiple data sites. The JAM system [11], intended for learning from such databases, is a distributed, scalable and portable agent-based data mining software package that employs a general approach to scaling data mining applications. JAM provides a set of learning programs that compute models from data stored locally at a site, and a set of methods for combining multiple models learned at different sites. However, the JAM software system doesn’t provide any tools for spatial data analysis.
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Computing environments for spatial data analysis

Computing environments for spatial data analysis

mechanisms to overcome the current lack of speed of the internet. Important questions remain about the division of labor between the server and the client in terms of the provided analytical capability. Many technical issues must be resolved before web delivery of analysis will be standard, but it is clearly an essential aspect of the analytical software tools of the future. Fourthly, the potential in terms of added functionality that could result from the fostering of a large community of developers in an open source context should not be underestimated. While it is unlikely that spatial data analysis will attract the same degree of attention as the maintenance and refinement of an operating system such as linux, the leverage of the input and commitment of many rather than a few could be significant. However, such a community can only exist if sufficient awareness and knowledge of the methods themselves has been generated, which is still far from being accomplished. Finally, there is likely to be an increasingly strong mutual reinforcement between spatial statistical and econometric methods and the computational tools to implement them in practice. For example, superior software tools for simulation have revolutionized the estimation of complex hierarchical models. Similarly, one can expect that significant advances in software tools for spatial data analysis will open up new opportunities for methodological and theoretical advances.
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Statistical Analysis of Spatial Dynamic Pattern in Spatial Data Analysis

Statistical Analysis of Spatial Dynamic Pattern in Spatial Data Analysis

We set the sample size to be 1500. The reason to set such a large sample size is because binary data carries much less information, and there are two unknown functions and two unknown constants to esti- mate in the model. In order to have enough information to construct decent estimators of the unknowns, we have to set the sample size in the magnitude of thousands. Because the computation involved in the model selection is very expensive, we only carry out 100 simulations. In each simulation, we compute the CV and AIC for each potential candidate model, and select the one with the smallest CV for the CV based approach. Similarly we select the smallest AIC for the AIC based approach. We find, in the 100 simulations, the ratio of picking the right model, M 1,2 , is 95% for the CV based approach, and 94%
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Implementing Spatial Data Analysis Software Tools in R

Implementing Spatial Data Analysis Software Tools in R

On the graphics side, R does not provide dynamic linked visualization, as the graphics model is based on drawing on one of a number of graphics devices. R does provide the more important tools for graphical data analysis, although no mapping is present as yet in general terms. The R spatial analysis Web site referred to above covers packages on CRAN for mapping using the legacy S format, shapefiles, and ArcInfo binary files, but each of these packages uses separate mechanisms and object representations at present. Panelled (Trellis) graphics are provided in the lattice package, and is now mature and makes available many of the tools needed for analyzing high-dimensional data in a reproducible fashion. This can be contrasted with dynamically linked visualization, which can be difficult to render as hard copy. Graphics are extensible at the user level in many ways, but are more for viewing and command-line interaction than for pointer-driven interaction.
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Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review

Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review

Finally, the use of categorical LULC information to parameterize ES assessments is a source of the generalization error associated with benefits transfer (see Section 3.1). LULC classes are not internally homogenous, instead exhibiting variation based upon the raster-based organization of features that are continuous on the landscape. Additionally, the spatial resolution of the imagery dictates the level of detail that is captured and can be reasonably used as category labels. The elements that vary con- tinuously, that will have an impact upon the range of conditions related in a given pixel, include species composition, the age, structure, and condition of the plant communities, and local variation in conditions related to land use or abiotic conditions, among other factors. Thus, overly coarse classes may obscure variation that is relevant to ESs (Koschke et al. 2013; Vihervaara et al. 2012). Hedonic pricing studies (that is, studies to determine what factors add value to properties, such as an ocean view, or access to natural areas, see Section 3.2) have shown that the type of open space and forested land cover present has significant effects on amenity values (e.g., Cho, Poudyal, and Roberts 2008). The assumption of homogeneity within a class is sometimes addressed by partitioning the study region into a larger number of very fine classes along such factors as ecosystem age and condition (e.g., Nelson et al. 2009). This strategy is problematic, however, as classification accuracy generally decreases sharply with increasing level of thematic detail (Fassnacht, Cohen, and Spies 2006; Remmel et al. 2005). Notwithstanding the need for accurate model inputs, Skidmore et al. (2011) note that error characteristics of input products are rarely considered in ES studies. Another challenge of high thematic resolu- tion LULC classifications in the ES mapping context is that the greater number of classes to be parameterized may result in unrealistic data requirements (Bagstad et al. 2012; Jackson et al. 2013; Nemec and Raudsepp-Hearne 2013).
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17 Interactive techniques and exploratory spatial data analysis

17 Interactive techniques and exploratory spatial data analysis

The most comprehensive set of tools to date that implement dynamic graphics for exploring spatial data is contained in the Regard (formerly Spider) software of Haslett, Unwin and associates, which runs on a Macintosh platform (see also Bradley and Haslett 1992; Haslett and Power 1995; Unwin 1994). Regard, and its successor Manet (Unwin et al 1996) allow for the visualisation of the distribution and associations between data for any subset of locations selected on a map display. Similarly, for any subset of data highlighted in a non-spatial view, such as a category in a histogram, the corresponding locations are highlighted on the map. This is illustrated in Figure 1, where attention focuses on suggesting promising multivariate relations pertaining to electoral change in the new German Bundesländer (formerly East Germany). Six types of dynamically linked views of the data are included, consisting of a map with highlighted constituencies, a bar chart,
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SARS Time Series Modeling and Spatial Data Analysis

SARS Time Series Modeling and Spatial Data Analysis

Development of GIS for epidemiological studies [4] plays an important role in providing a new technique, using geographic information system's powerful data management capabilities and display functions, you can get more information than an ordinary map, such as in SARS control, with ordinary map to find areas within 100 meters of HIV is more difficult, but can quickly GIS buffer at 100 meters, and select out qualifying place.

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Spatial data analysis: retrieval, (re)classification and measurement operations

Spatial data analysis: retrieval, (re)classification and measurement operations

A raster map with the domain type value cannot be reclassified by the method explained in the previous section, since you cannot attach a table to it. Take for example a Digital Elevation Model (DEM). This is a value map which contains a wide range of values. The map DEM in the demo data set, for example, contains values between 2520 and 4560, with a precision of 0.1. Suppose we would like to classify this map into five different height zones. It would require a very large table, with an attribute column containing a lot of repetitive values.

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EXPLORATORY SPATIAL DATA ANALYSIS TECHNIQUES FOR EXAMINING URBAN CRIME

EXPLORATORY SPATIAL DATA ANALYSIS TECHNIQUES FOR EXAMINING URBAN CRIME

The use of GIS and crime mapping software for studying criminal activity has generally been in the area of environmental criminology, which is concerned with the physical characteristics of areas and their impact on preventing or encouraging crime (see Harries 1980; Brantingham and Brantingham 1981). The relationship between the physical layout of an area, proximity to various services, and land use mixes are seen as important factors which are likely to influence criminal behaviour (Greenburg and Rohe 1984). Environmental criminologists have proposed that certain crimes are more likely to be perpetrated because of issues of access, exposure and opportunity (Brantingham and Brantingham 1981; Byrne and Sampson 1986). That is, significantly high numbers of property crimes or robberies, as an example, may be a result of the design of a suburb, where there are residential and industrial land use mixes, building entrances not visible from main roads, etc. In general, a major limitation of the application of GIS and crime mapping software has been its descriptive use for depicting crime activity rather than any focus on data integration issues or the development of analytically sophisticated tools.
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Geographical Information Systems, Spatial Data Analysis and Decision Making in Government

Geographical Information Systems, Spatial Data Analysis and Decision Making in Government

From the work summarised in this thesis, Roger’s later achievements in the application of GIS were summarised in the May 2001 citation for his award of the Order of Canada: Recognized as the 'father o f GIS', he pioneered its uses worldwide to collect, manage and manipulated geographical data, changing the face o f geography as a discipline. His work established Canada's reputation fo r excellence in the emerging and continually expanding field o f geo-spatial analysis. Governments and scientists around the world have turned to him to better understand our environment and changing patterns o f land use, to better manage urban development and our precious natural resources.
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Spatial Data Mining Analysis Methods

Spatial Data Mining Analysis Methods

These days, information investigation in topography is basically taking into account conventional measurements and multidimensional information examination and does not make note of spatial information [3]. However the principle specificity of geographic information is that perceptions situated close to each other in space tend to have comparative (or associated) characteristic qualities. This constitutes the principal of an unmistakable exploratory territory called "spatial measurements" which, not at all like conventional insights, assumes between reliance of adjacent perceptions. An inexhaustible list of sources exists around there, including understood geostatistics, late improvements in Exploratory Spatial Data Analysis (ESDA) by Anselin and Geographical Analysis Machine (GAM) by Openshaw [4]. Multi-dimensional scientific strategies have been reached out to bolster contiguity [5, 6]. We keep up that spatial measurements is a piece of spatial information mining, since it gives information driven examinations. Some of those strategies are currently executed in operational GIS or investigation instruments.
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GEOCOMPUTATION TECHNIQUES FOR SPATIAL ANALYSIS: IS IT THE CASE FOR HEALTH DATA SETS?

GEOCOMPUTATION TECHNIQUES FOR SPATIAL ANALYSIS: IS IT THE CASE FOR HEALTH DATA SETS?

Simply defined, geocomputation "is the process of applying computing technology to geographical problems". As Oppenshaw (1996) points out, "many end- users merely want answers to fairly abstract questions such as 'Are there any patterns, where are they, and what do they look like ?'". This definition, although generic, point to a number of motivating factors: the emergence of computerised data-rich environments, affordable computational power, and spatial data analysis and mining techniques.

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Current practices in spatial analysis of cancer data: data characteristics and data sources for geographic studies of cancer

Current practices in spatial analysis of cancer data: data characteristics and data sources for geographic studies of cancer

The Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute (NCI) offers county-level incidence data for its member registries, which cover part or all of eight states, through its SEER*Stat software. Because it provides direct access to individual cancer records, users must first sign a data access agreement. County-level mortality data for the entire United States, collected and maintained by the National Center for Health Statistics (NCHS), is also accessible through SEER*Stat. These data include all causes of death, not just cancer deaths. Selected county- level cancer data may also be accessed through the NCI's Cancer Mortality Maps and Graphs and State Cancer Pro- files web sites. The latter was launched in 2003 and con- tains a host of innovative statistical graphics. Many individual state registries also offer additional geographi- cally referenced data. For example, the Florida Cancer Data System web site allows users to generate a variety of county- and facility-level tables and county-level maps on demand. The Kentucky Cancer Registry also offers a county-level mapping application. New York State offers a limited set of ZIP code level data for the four most com- mon cancer types in the mid-1990s. Currently, county- level cancer incidence data is not available nationally. 2. Population data
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Spatial data quality

Spatial data quality

This report emphasizes that cartographic quality control cannot be considered an option in the creation of a GIS. A highly skilled cartographic editor should be assigned whose sole responsibility is executing a strategic plan to ensure that accuracy, completeness, consistency and aesthetics are monitored continuously throughout GIS development. Successful quality control programs encompass editing aerial photography; gathering quality control materials, editing compiled information, checking aesthetics, ensuring that map sheets match, editing contours, reviewing corrected data, generating client review plots, and reviewing and submitting the final GIS products. Tools for quality control includes check plots, photographic enlargements, existing source documents, score sheets, data layer validation programs, and quality control process. More and more municipalities, utility companies, and other organizations only select GIS contractors with proven quality control programs.
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Analysis Of Secure Geometric Range Search Over Encrypted Spatial Data

Analysis Of Secure Geometric Range Search Over Encrypted Spatial Data

A BSTRA CT: The overall subject matter of the cloud data is existing on the remote server is to be controlled with min imal co mputation by means of data owner and data users. The data is stored away in encrypted form to save our anonymity activities. Reachability is one of the issues faced by cloud customers and LBS groups. A novel and light-weight scheme, named, Geo metric Range Search Model (GRSM ) that retrieves the quest data from cipher text dataset. The information is taken into consideration as factors and the group of factors denotes the cipher text database. Bloo m filter is the that includes all viable comb ination of seeking tokens. The proposed GRSM comp rise three levels, namely, Encryption segment, Token technology segment and Search Each section serves as input/ output to retrieve the search statistics. An investigational result shows the effectiveness of the proposed set of rules.
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Geospatial crowdsourced data fitness analysis for spatial
data infrastructure based disaster management actions

Geospatial crowdsourced data fitness analysis for spatial data infrastructure based disaster management actions

The quality of geospatial data is critical to deciding the quality of the outcomes of any project or application concerned. Spatial data quality assessment techniques and pa- rameters have long been considered among mapping/GIS professionals, academics and researchers. However, general spatial data quality matrices are not applicable in measuring the quality of CSD due to structural, procedural and technological discrep- ancies as well as missing metadata. As a solution, various quality indicators have been proposed such as relevance and credibility. Credibility issues arise as CSD come from heterogeneous sources and are captured both by professionals and amateurs. The rele- vance or fitness for the purpose is highly cognitive and depends on the task at hand. This chapter presented CSD credibility and relevance assessment approaches. A cred- ibility assessment method using a naïve Bayesian Network based model which is com- monly used in spam email detection systems was explored. This chapter also investi- gated a relevance assessment approach by adapting relevance assessment techniques available in the GIR domain. The thematic and geographic relevance assessment meth- ods using the TF-IDF VSM, NLP based semantic gazetteers lookup were discussed along with the use of thematic and geographic specificity of the queries for relevance ranking. This chapter also explained the GIR concepts, use of NLP techniques, seman- tics, ontologies and gazetteers for GIR. The utilisation of an ontological gazetteer, GATE software and its components were discussed.
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