Stevens County GIS Data 2009

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Thurston County GIS Data 2012

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Thurston County GIS Data 2011

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Thurston County GIS Data 2007

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Thurston County GIS Data 2004

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Thurston County GIS Data 1999

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Thurston County GIS Data 1998

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Yakima County GIS Data 2007

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Yakima County GIS Data 2010

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Yakima County Assessor Data 2010

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

University of Washington GIS Data 2013

UW access only. Questions about spatial data can be directed to uwlib-gis [at] uw [dot] edu, include the URI address below and any information you have.

Spatial analysis of crime evolution in Portugal between 1995 and 2013

The main objective of this survey was to perform descriptive analysis of crime evolution in Portugal between 1995 and 2013. The main focus of this survey was to analyse spatial crime evolution patterns in Portuguese NUTS III regions. Most important crime types have been included into analysis. The main idea was to uncover relation between local patterns and global crime evolution; to define regions which have contributed to global crime evolution of some specific crime types and to define how they have contributed. There were many statistical reports and scientific papers which have analysed some particular crime types, but one global spatial-temporal analysis has not been found. Principal Component Analysis and multidimensional descriptive data analysis technique STATIS have been the base of the analysis. The results of this survey has shown that strong spatial and temporal crime patterns exist. It was possible to describe global crime evolution patterns and to define crime evolution patterns in NUTS III regions. It was possible to define three to four groups of crimes where each group shows similar spatial crime dynamics.

Generating Relation Algebras for Qualitative Spatial Reasoning

Basic relationships between certain regions of space are formulated in natural language in everyday situations. For example, a customer specifies the outline of his future home to the architect by indicating which rooms should be close to each other. Qualitative spatial reasoning as an area of artificial intelligence tries to develop a theory of space based on similar notions. In formal ontology and in ontological computer science, mereotopology is a first-order theory, embodying mereological and topological concepts, of the relations among wholes, parts, parts of parts, and the boundaries between parts. We shall introduce abstract relation algebras and present their structural properties as well as their connection to algebras of binary relations. This will be followed by details of the expressiveness of algebras of relations for region based models. Mereotopology has been the main basis for most region based theories of space. Since its earliest inception many theories have been proposed for mereotopology in artificial intelligence among which Region Connection Calculus is most prominent. The expressiveness of the region connection calculus in relational logic is far greater than its original eight base relations might suggest. In the thesis we formulate ways to automatically generate representable relation algebras using spatial data based on region connection calculus. The generation of new algebras is a two pronged approach involving splitting of existing relations to form new algebras and refinement of such newly generated algebras. We present an implementation of a system for automating aforementioned steps and provide an effective and convenient interface to define new spatial relations and generate representable relational algebras.

SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining

Are the spatial patterns of weeds scale-invariant?

In previous empirical and modelling studies of rare species and weeds, evidence of fractal behaviour has been found. We propose that weeds in modern agricultural systems may be managed close to critical population dynamic thresholds, below which their rates of increase will be negative and where scale-invariance may be expected as a consequence. We collected detailed spatial data on five contrasting species over a period of three years in a primarily arable field. Counts in 20×20 cm contiguous quadrats, 225,000 in 1998 and 84,375 thereafter, could be re-structured into a wide range of larger quadrat sizes. These were analysed using three methods based on correlation sum, incidence and conditional incidence. We found non-trivial scale invariance for species occurring at low mean densities and where they were strongly aggregated. The fact that the scale-invariance was not found for widespread species occurring at higher densities suggests that the scaling in agricultural weed populations may, indeed, be related to critical phenomena.