The development of specialized software for spatial data analysis has seen rapid growth since the lack of such tools was lamented in the late 1980s by Haining (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 environment (loosely vs. tightly coupled, embedded vs. modular, etc.), and which techniques would be most fruitfully included in such a framework. Familiar reviews of these issues are represented in, among others, Anselin and Getis (1992), Goodchild et al. (1992), Fischer and Nijkamp (1993), Fotheringham and Rogerson (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 burgeoning open source effort using software environments such as R, Java and Python. This is exemplified by the growing contents of the software tools clearing house maintained by the U.S.- based Center for Spatially Integrated Social Science [CSISS] (see http://www.csiss.org/clearinghouse/).

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0016-7363.2005.00671.x

GeoDa: An Introduction to Spatial Data Analysis

羟氨苄青霉素引起大疱性类天疱疮样疹

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

An Introduction to Spatial Data Analysis

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.1002/cncr.32052

GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology.

Cancer control researchers seek to reduce the burden of cancer by studying interventions, their impact in defined populations, and the means by which they can be better used. The first step in cancer control is identifying where the cancer burden is elevated, which suggests locations where interventions are needed. Geographic information systems (GIS) and other spatial analytic methods provide such a solution and thus can play a major role in cancer control. This report presents findings from a workshop held June 16–17, 2005, to bring together experts and stakeholders to address current issues in GIScience and cancer control. A broad range of areas of expertise and interest was represented, including epidemiology, geography, statistics, environmental health, social science, cancer control, cancer registry operations, and cancer advocacy. The goals of this workshop were to build consensus on important policy and research questions, identify roadblocks to future progress in this field, and provide recommendations to overcome these roadblocks.

/pdf/the-crossroads-of-gis-and-health-information-a-workshop-on-3mvchmlwa2.pdf

The crossroads of GIS and health information: a workshop on developing a research agenda to improve cancer control

An Introduction to Micromaps Introduction Row-labeled plots Linked micromaps Conditioned micromaps Comparative micromaps Summary and preview of book chapters Research Influencing Micromap Design Influence of statistical graphics research on micromap designs Contributions from other research areas Human perceptual and cognitive strengths and limitations impacting data visualization Summary Data Visualization Design Principles Introduction Enabling accurate comparisons Strive for simple appearance Engage the analyst Summary Linked Micromaps Introduction Page layout Data encodings Micromap highlighting Multivariate data Multivariate sorting Pushing the envelope Software Summary Conditioned Micromaps Introduction One-way conditioned layouts Two-way layouts Higher order layouts Describing and comparing groups of regions Weighted descriptions and comparisons Alternative views CCmaps software options Summary Comparative Micromaps Introduction Representing change Types of comparisons Two-way comparisons Rates of change Alternative views Summary and future directions Putting It All Together Summary Exploration of Louisiana population changes after the 2005 hurricanes Concluding remarks Appendix 1: Data sources and notes Appendix 2: Symmetric perceptual groupings References Index

Visualizing Data Patterns with Micromaps

This paper introduces interactive extensions to two recently developed templates for displaying geospatially-indexed estimates. The first template, linked micromap plots, links small generalized maps with statistical panels that describe regions. Research centered at the National Cancer Institute addressed the task of communicating state and county cancer statistics and tailored this template to show estimates, confidence intervals, and Healthy People 2010 target values. The research also integrated interactive options, such as variable selection, sorting, fixed header scrolling, mouse tips, enlarged dynamic map views and drill down, in a Java applet. This template has fared well in early usability tests. The second template, called conditioned choropleth maps, seeks to improve hypothesis generation about the spatial patterns shown in a classed choropleth map. Since variation of a study variable is often related to known risk factors, the template provides a way to control for the known variation. This paper describes dynamic sliders that change class boundaries for a study variable and that partition regions into a 3 x 3 layout of maps based on values of two risk factors. Highlighted regions in each map are more homogeneous with respect to both risk factors. Comparisons across maps and spatial patterns within maps provide the basis for generating hypotheses. The JAVA application shareware also includes dynamic statistical annotation and QQplots for comparing distributions

Interactive linked micromap plots and dynamically conditioned choropleth maps

Over the last decade, researchers have developed many improvements to make statistical graphics more accessible to the general public. These improvements include making statistical summaries more visual and providing more information at the same time. Research in this area involved converting statistical tables into plots (Carr, 1994; Carr and Nusser, 1995), new ways of displaying geographically referenced data (Carr et al., 1992), and, inparticular, the developmentof linkedmicromap(LM)plots, often simply called micromaps (Carr and Pierson, 1996; Carr et al., 1998, 2000a). LM plots, initially called map row plots as well as linked map-attribute graphics, were first presented in a poster session sponsored by the American Statistical Association (ASA) Section on Statistical Graphics at the 1996 Joint Statistical Meetings (Olsen et al., 1996).More details on the history of LMplots and their connection to other research can be found in these early references on micromaps. More recent references on LM plots (Carr et al., 2000b; Carr, 2001) focused on their use for communicating summary data from health and environmental studies.

Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data

Linked micromap (LM) plots offer a new template for displaying spatially indexed statistical summaries. One can use LM plots to visualize complex data in many areas. This paper introduces web-based interactive LM plots, a statistical data visualization system that integrates geographical data manipulation, visualization, interactive statistical graphics, and web-based Java technologies. The system effectively presents the complex and large-volume sample data of national cancer statistics of the United States.

Geographic statistics visualization: web-based linked micromap plots

The National Cancer Institute's State Cancer Profiles web site that was launched in mid-April put the power of new graphical tools into the hands of cancer control planners and the public. This paper emphasizes advances in two graphics templates, one heavily used in the web site and one planned for future use. These templates for showing geospatially-indexed statistics address needs that are less well addressed by more conventional maps, such as the representation of estimate uncertainty. The web site and templates both evolved through extensive usability testing of targeted users. The results described in this paper signal highly visible acceptance of the new interactive and dynamic forms of these templates.

The State Cancer Profiles web site and extensions of linked micromap plots and conditioned choropleth map plots

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