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Uncertainty and context pose fundamental challenges in GIScience
and geographic research. Geospatial data are imbued with errors
(e.g., measurement and sampling) and various types of uncertainty
that often obfuscate any understanding of the effects of contextual
or environmental influences on human behaviors and experiences.
These errors or uncertainties include those attributable to
geospatial data measurement, model specifications, delineations of
geographic context in space and time, and the use of different
spatiotemporal scales and zonal schemes when analyzing the effects
of environmental influences on human behaviors or experiences. In
addition, emerging sources of geospatial big data – including
smartphone data, data collected by GPS, and various types of
wearable sensors (e.g., accelerometers and air pollutant monitors),
volunteered geographic information, and/ or location- based social
media data (i.e., crowd- sourced geographic information) –
inevitably contain errors, and their quality cannot be fully
controlled during their collection or production. Uncertainty and
Context in GIScience and Geography: Challenges in the Era of
Geospatial Big Data illustrates how cutting- edge research explores
recent advances in this area, and will serve as a useful point of
departure for GIScientists to conceive new approaches and solutions
for addressing these challenges in future research. The seven core
chapters in this book highlight many challenges and opportunities
in confronting various issues of uncertainty and context in
GIScience and geography, tackling different topics and approaches.
The chapters in this book were originally published as a special
issue of the International Journal of Geographical Information
Science.
Uncertainty and context pose fundamental challenges in GIScience
and geographic research. Geospatial data are imbued with errors
(e.g., measurement and sampling) and various types of uncertainty
that often obfuscate any understanding of the effects of contextual
or environmental influences on human behaviors and experiences.
These errors or uncertainties include those attributable to
geospatial data measurement, model specifications, delineations of
geographic context in space and time, and the use of different
spatiotemporal scales and zonal schemes when analyzing the effects
of environmental influences on human behaviors or experiences. In
addition, emerging sources of geospatial big data - including
smartphone data, data collected by GPS, and various types of
wearable sensors (e.g., accelerometers and air pollutant monitors),
volunteered geographic information, and/ or location- based social
media data (i.e., crowd- sourced geographic information) -
inevitably contain errors, and their quality cannot be fully
controlled during their collection or production. Uncertainty and
Context in GIScience and Geography: Challenges in the Era of
Geospatial Big Data illustrates how cutting- edge research explores
recent advances in this area, and will serve as a useful point of
departure for GIScientists to conceive new approaches and solutions
for addressing these challenges in future research. The seven core
chapters in this book highlight many challenges and opportunities
in confronting various issues of uncertainty and context in
GIScience and geography, tackling different topics and approaches.
The chapters in this book were originally published as a special
issue of the International Journal of Geographical Information
Science.
This book contains refereed papers from the 13th International
Conference on GeoComputation held at the University of Texas,
Dallas, May 20-23, 2015. Since 1996, the members of the
GeoComputation (the art and science of solving complex spatial
problems with computers) community have joined together to develop
a series of conferences in the United Kingdom, New Zealand,
Australia, Ireland and the United States of America. The conference
encourages diverse topics related to novel methodologies and
technologies to enrich the future development of GeoComputation
research.
This book contains refereed papers from the 13th International
Conference on GeoComputation held at the University of Texas,
Dallas, May 20-23, 2015. Since 1996, the members of the
GeoComputation (the art and science of solving complex spatial
problems with computers) community have joined together to develop
a series of conferences in the United Kingdom, New Zealand,
Australia, Ireland and the United States of America. The conference
encourages diverse topics related to novel methodologies and
technologies to enrich the future development of GeoComputation
research.
Spatial Regression Analysis Using Eigenvector Spatial Filtering
provides theoretical foundations and guides practical
implementation of the Moran eigenvector spatial filtering (MESF)
technique. MESF is a novel and powerful spatial statistical
methodology that allows spatial scientists to account for spatial
autocorrelation in their georeferenced data analyses. Its appeal is
in its simplicity, yet its implementation drawbacks include serious
complexities associated with constructing an eigenvector spatial
filter. This book discusses MESF specifications for various
intermediate-level topics, including spatially varying coefficients
models, (non) linear mixed models, local spatial autocorrelation,
space-time models, and spatial interaction models. Spatial
Regression Analysis Using Eigenvector Spatial Filtering is
accompanied by sample R codes and a Windows application with
illustrative datasets so that readers can replicate the examples in
the book and apply the methodology to their own application
projects. It also includes a Foreword by Pierre Legendre.
"Ideal for anyone who wishes to gain a practical understanding of
spatial statistics and geostatistics. Difficult concepts are well
explained and supported by excellent examples in R code, allowing
readers to see how each of the methods is implemented in practice"
- Professor Tao Cheng, University College London Focusing
specifically on spatial statistics and including components for
ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic
spatial statistics and geostatistics, as well as the spatial
filtering techniques used in all relevant programs and software. It
explains and demonstrates techniques in: spatial sampling spatial
autocorrelation local statistics spatial interpolation in
two-dimensions advanced topics including Bayesian methods, Monte
Carlo simulation, error and uncertainty. It is a systematic
overview of the fundamental spatial statistical methods used by
applied researchers in geography, environmental science, health and
epidemiology, population and demography, and planning. A companion
website includes digital R code for implementing the analyses in
specific chapters and relevant data sets to run the R codes.
"Ideal for anyone who wishes to gain a practical understanding of
spatial statistics and geostatistics. Difficult concepts are well
explained and supported by excellent examples in R code, allowing
readers to see how each of the methods is implemented in practice"
- Professor Tao Cheng, University College London Focusing
specifically on spatial statistics and including components for
ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic
spatial statistics and geostatistics, as well as the spatial
filtering techniques used in all relevant programs and software. It
explains and demonstrates techniques in: spatial sampling spatial
autocorrelation local statistics spatial interpolation in
two-dimensions advanced topics including Bayesian methods, Monte
Carlo simulation, error and uncertainty. It is a systematic
overview of the fundamental spatial statistical methods used by
applied researchers in geography, environmental science, health and
epidemiology, population and demography, and planning. A companion
website includes digital R code for implementing the analyses in
specific chapters and relevant data sets to run the R codes.
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