File Name: use of gis in spatial data analysis and modelling .zip
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A range of GIS tools and resources are available below to help assessors create distribution maps in a format appropriate for publication in an IUCN Red List assessment. This page will be updated with new tools as and when they are created. Creates a minmum convex polygon to calculate extent of occurrence EOO.
Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological , geometric , or geographic properties. Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy , with its studies of the placement of galaxies in the cosmos , to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures.
In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data.
Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. The most fundamental of these is the problem of defining the spatial location of the entities being studied.
Classification of the techniques of spatial analysis is difficult because of the large number of different fields of research involved, the different fundamental approaches which can be chosen, and the many forms the data can take. Spatial analysis began with early attempts at cartography and surveying. Land surveying goes back to at least 1, B.
C in Egypt: the dimensions of taxable land plots were measured with measuring ropes and plumb bobs. Many fields have contributed to its rise in modern form. Biology contributed through botanical studies of global plant distributions and local plant locations, ethological studies of animal movement, landscape ecological studies of vegetation blocks, ecological studies of spatial population dynamics, and the study of biogeography.
Epidemiology contributed with early work on disease mapping, notably John Snow 's work of mapping an outbreak of cholera, with research on mapping the spread of disease and with location studies for health care delivery. Statistics has contributed greatly through work in spatial statistics.
Economics has contributed notably through spatial econometrics. Geographic information system is currently a major contributor due to the importance of geographic software in the modern analytic toolbox. Remote sensing has contributed extensively in morphometric and clustering analysis. Computer science has contributed extensively through the study of algorithms, notably in computational geometry. Mathematics continues to provide the fundamental tools for analysis and to reveal the complexity of the spatial realm, for example, with recent work on fractals and scale invariance.
Scientific modelling provides a useful framework for new approaches. Spatial analysis confronts many fundamental issues in the definition of its objects of study, in the construction of the analytic operations to be used, in the use of computers for analysis, in the limitations and particularities of the analyses which are known, and in the presentation of analytic results. Many of these issues are active subjects of modern research. Common errors often arise in spatial analysis, some due to the mathematics of space, some due to the particular ways data are presented spatially, some due to the tools which are available.
Census data, because it protects individual privacy by aggregating data into local units, raises a number of statistical issues. The fractal nature of coastline makes precise measurements of its length difficult if not impossible. A computer software fitting straight lines to the curve of a coastline, can easily calculate the lengths of the lines which it defines.
However these straight lines may have no inherent meaning in the real world, as was shown for the coastline of Britain. These problems represent a challenge in spatial analysis because of the power of maps as media of presentation.
When results are presented as maps, the presentation combines spatial data which are generally accurate with analytic results which may be inaccurate, leading to an impression that analytic results are more accurate than the data would indicate. The definition of the spatial presence of an entity constrains the possible analysis which can be applied to that entity and influences the final conclusions that can be reached.
While this property is fundamentally true of all analysis , it is particularly important in spatial analysis because the tools to define and study entities favor specific characterizations of the entities being studied. Statistical techniques favor the spatial definition of objects as points because there are very few statistical techniques which operate directly on line, area, or volume elements. Computer tools favor the spatial definition of objects as homogeneous and separate elements because of the limited number of database elements and computational structures available, and the ease with which these primitive structures can be created.
Spatial dependence is the spatial relationship of variable values for themes defined over space, such as rainfall or locations for themes defined as objects, such as cities. Spatial dependence is measured as the existence of statistical dependence in a collection of random variables , each of which is associated with a different geographical location. Spatial dependence is of importance in applications where it is reasonable to postulate the existence of corresponding set of random variables at locations that have not been included in a sample.
Thus rainfall may be measured at a set of rain gauge locations, and such measurements can be considered as outcomes of random variables, but rainfall clearly occurs at other locations and would again be random. Because rainfall exhibits properties of autocorrelation , spatial interpolation techniques can be used to estimate rainfall amounts at locations near measured locations. As with other types of statistical dependence, the presence of spatial dependence generally leads to estimates of an average value from a sample being less accurate than had the samples been independent, although if negative dependence exists a sample average can be better than in the independent case.
A different problem than that of estimating an overall average is that of spatial interpolation : here the problem is to estimate the unobserved random outcomes of variables at locations intermediate to places where measurements are made, on that there is spatial dependence between the observed and unobserved random variables. Tools for exploring spatial dependence include: spatial correlation , spatial covariance functions and semivariograms.
Methods for spatial interpolation include Kriging , which is a type of best linear unbiased prediction. The topic of spatial dependence is of importance to geostatistics and spatial analysis. Spatial dependency is the co-variation of properties within geographic space: characteristics at proximal locations appear to be correlated, either positively or negatively.
Spatial dependency leads to the spatial autocorrelation problem in statistics since, like temporal autocorrelation, this violates standard statistical techniques that assume independence among observations. For example, regression analyses that do not compensate for spatial dependency can have unstable parameter estimates and yield unreliable significance tests. Spatial regression models see below capture these relationships and do not suffer from these weaknesses.
It is also appropriate to view spatial dependency as a source of information rather than something to be corrected. Locational effects also manifest as spatial heterogeneity , or the apparent variation in a process with respect to location in geographic space. Unless a space is uniform and boundless, every location will have some degree of uniqueness relative to the other locations. This affects the spatial dependency relations and therefore the spatial process.
Spatial heterogeneity means that overall parameters estimated for the entire system may not adequately describe the process at any given location.
Spatial association is the degree to which things are similarly arranged in space. Analysis of the distribution patterns of two phenomena is done by map overlay. If the distributions are similar, then the spatial association is strong, and vice versa.
For example, a set of observations as points or extracted from raster cells at matching locations can be intersected and examined by regression analysis. Like spatial autocorrelation , this can be a useful tool for spatial prediction.
In spatial modeling, the concept of spatial association allows the use of covariates in a regression equation to predict the geographic field and thus produce a map. Spatial measurement scale is a persistent issue in spatial analysis; more detail is available at the modifiable areal unit problem MAUP topic entry. Landscape ecologists developed a series of scale invariant metrics for aspects of ecology that are fractal in nature. Spatial sampling involves determining a limited number of locations in geographic space for faithfully measuring phenomena that are subject to dependency and heterogeneity.
But heterogeneity suggests that this relation can change across space, and therefore we cannot trust an observed degree of dependency beyond a region that may be small. Basic spatial sampling schemes include random, clustered and systematic.
These basic schemes can be applied at multiple levels in a designated spatial hierarchy e. It is also possible to exploit ancillary data, for example, using property values as a guide in a spatial sampling scheme to measure educational attainment and income. Spatial models such as autocorrelation statistics, regression and interpolation see below can also dictate sample design.
The fundamental issues in spatial analysis lead to numerous problems in analysis including bias, distortion and outright errors in the conclusions reached. These issues are often interlinked but various attempts have been made to separate out particular issues from each other.
In discussing the coastline of Britain , Benoit Mandelbrot showed that certain spatial concepts are inherently nonsensical despite presumption of their validity. Lengths in ecology depend directly on the scale at which they are measured and experienced. So while surveyors commonly measure the length of a river, this length only has meaning in the context of the relevance of the measuring technique to the question under study. The locational fallacy refers to error due to the particular spatial characterization chosen for the elements of study, in particular choice of placement for the spatial presence of the element.
Spatial characterizations may be simplistic or even wrong. Studies of humans often reduce the spatial existence of humans to a single point, for instance their home address. This can easily lead to poor analysis, for example, when considering disease transmission which can happen at work or at school and therefore far from the home.
The spatial characterization may implicitly limit the subject of study. For example, the spatial analysis of crime data has recently become popular but these studies can only describe the particular kinds of crime which can be described spatially.
This leads to many maps of assault but not to any maps of embezzlement with political consequences in the conceptualization of crime and the design of policies to address the issue. This describes errors due to treating elements as separate 'atoms' outside of their spatial context.
The fallacy is about transferring individual conclusions to spatial units. The ecological fallacy describes errors due to performing analyses on aggregate data when trying to reach conclusions on the individual units.
For example, a pixel represents the average surface temperatures within an area. Ecological fallacy would be to assume that all points within the area have the same temperature. This topic is closely related to the modifiable areal unit problem. A mathematical space exists whenever we have a set of observations and quantitative measures of their attributes.
For example, we can represent individuals' incomes or years of education within a coordinate system where the location of each individual can be specified with respect to both dimensions. The distance between individuals within this space is a quantitative measure of their differences with respect to income and education.
However, in spatial analysis, we are concerned with specific types of mathematical spaces, namely, geographic space. In geographic space, the observations correspond to locations in a spatial measurement framework that capture their proximity in the real world. The locations in a spatial measurement framework often represent locations on the surface of the Earth, but this is not strictly necessary.
A spatial measurement framework can also capture proximity with respect to, say, interstellar space or within a biological entity such as a liver. The fundamental tenet is Tobler's First Law of Geography : if the interrelation between entities increases with proximity in the real world, then representation in geographic space and assessment using spatial analysis techniques are appropriate.
The Euclidean distance between locations often represents their proximity, although this is only one possibility. There are an infinite number of distances in addition to Euclidean that can support quantitative analysis. For example, "Manhattan" or " Taxicab " distances where movement is restricted to paths parallel to the axes can be more meaningful than Euclidean distances in urban settings. In addition to distances, other geographic relationships such as connectivity e.
It is also possible to compute minimal cost paths across a cost surface; for example, this can represent proximity among locations when travel must occur across rugged terrain. Spatial data comes in many varieties and it is not easy to arrive at a system of classification that is simultaneously exclusive, exhaustive, imaginative, and satisfying.
Fingelton [11]. Urban and Regional Studies deal with large tables of spatial data obtained from censuses and surveys.
No matter what your interests are or what field you work in, spatial data is always being considered whether you know it or not. Spatial data can exist in a variety of formats and contains more than just location specific information. To properly understand and learn more about spatial data, there are a few key terms that will help you become more fluent in the language of spatial data. Vector data is best described as graphical representations of the real world. There are three main types of vector data: points, lines, and polygons. Connecting points create lines, and connecting lines that create an enclosed area create polygons. Vector data and the file format known as shapefiles.
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. Lisa Wainger, research professor at the University of Maryland, discussed optimization modeling to analyze multi-resource management goals. She identified three elements to developing scenarios: economic efficiency, legal compliance, and social equity. Economic efficiency states that parcels resources will be utilized at their highest and best use. In practice, this translates to landowners using their land in a way that provides the highest income over time. Increasingly, however, public agencies are finding opportunities for preserving more land.
Although GIS and spatial data analysis started out as two more or less representation of certain types of spatial structure), test for model techniques and models that explicitly use the spatial referencing of each data case.
Spatial Analysis, Modelling and Planning. It can be difficult to separate spatial analysis from other fields of interest such as geography, location analysis, geographic information science, etc. Yet, its beginnings are to some extent easy to identify.
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Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological , geometric , or geographic properties. Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy , with its studies of the placement of galaxies in the cosmos , to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research.
Spatial analysis extracts or creates new information from spatial data". Spatial Analysis skills have many uses ranging from emergency management and other city services, business location and retail analysis, transportation modeling, crime and disease mapping, and natural resource management. Spatial analysis answers where questions.
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