Kang-tsung Chang. The interpolators such as TIN (Triangular Irregular Networks), IDW (Inverse Distance Weighted), RBF (Radial Basis Function) and LPI (Local Polynomial Interpolation), are classified as deterministic methods. Bilinear interpolation technique has been used in this. Introduction. Geographic Information System (GIS) is a computer based information system used to digitally represent and analyse the geographic features. 1 A gentle introduction to GIS Computer representations of geographic information. .. The book is also made available as an electronic PDF document.
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vitecek.infoli - Introduction to. Geographic Information Systems. 3. We define GIS ( Geographic Information System) as a structure constituted by a powerful set of. References. Cartensen, Laurence W. and Henry, Norah F. Digital Mapping and Geographic Analysis: An. Introduction to Geographic Information systems. 1) Introduction. This manual was prepared for GIS training courses organised by the Crop Crisis Control. Project (C3P) in the “Great Lakes” region of East Africa.
John P. Wilson A.
Stewart Fotheringham. First published: Print ISBN: About this book This Handbook is an essential reference and a guide to the rapidly expanding field of Geographic Information Science. Designed for students and researchers who want an in-depth treatment of the subject, including background information Comprises around 40 substantial essays, each written by a recognized expert in a particular area Covers the full spectrum of research in GIS Surveys the increasing number of applications of GIS Predicts how GIS is likely to evolve in the near future.
Author Bios John P. Free Access. Summary PDF Request permissions. PDF Request permissions. Tools Get online access For authors. Email or Customer ID. Using GIS, adjacency can be measured in number of ways, including a binary yes 1 or no 0 , or by the length of the shared boundary. The figure 4 example provides a contiguity matrix of binary adjacency for zip code area This adjacency matrix would be needed for many types of spatial statistics.
Also note that some geographic models assess lagged relationships i. Essential to the use of GIS is the ability to overlay multiple layers of information and access these various layers simultaneously. Figure 5 shows an example of the overlay function. Points, such as crime locations, lines, such as major roadways, and polygons, such as police districts, are combined into a single digital map by the GIS.
GIS can also count the number of crimes in each district in the final overlay. The counting of events or places within a specific geographic area is often needed to facilitate multi-level hierarchical models [ 32 ].
The overlay function facilitates spatial analysis by the ready creation of combinations of information, by creating new forms of information by allocating points to areas for a new area-based metric e.
For example, if one wanted to find a location that was within a specific police district, within a specific distance of a main road, and also a specific distance from a the nearest crime, a query could be written to locate the places that meet those requirements.
A spatial buffer identifies a specified area around a specific geographic feature. Buffers are useful for identifying neighborhood-related factors for decision-making e. Buffers combine the distance measurement capability of GIS by applying it to various features.
Figure 6 shows a radial buffer with the Buffalo State College address as the centroid. GIS buffers would enable counts of students or housing or whatever other layers of data that could be available. The use for policy and planning for this function is obvious, but hard to duplicate without a GIS.
Figure 7 shows a buffer around a line feature, in this case a road that has public transportation. This figure illustrates how a buffer can follow the shape of a more complex feature. The buffer could be used to identify patients or workers that have ready access to public transportation. One can easily imagine how spatial buffers can be combined with multiple overlays and complex queries to facilitate geographic decisions and to create measures that could be used in a variety of statistical or modeling applications.
GIS provides the capability of reclassifying data in an automated manner. The example in figure 8 is based on reclassifying one specific attribute poverty rate in four different ways. Map a in the figure shows an equal interval distribution. Equal interval classes are based on creating categories of the attribute that are defined by an equivalent range e.
Note that an equal interval classification does not usually result in an equal proportion of the distribution in each category, as can been seen in figure 8a. Figure 8b shows a classification based on using natural breaks, which utilizes the distributional characteristics of the attribute data to create categories that reflect the majority of the areas as middle ranges and the extremes of the distribution as smaller number of areas.
Figures 8c and 8d are based on quantile classifications, which allocate the areas into categories that consist of an equal proportion of the areas in each category. Figure 8c uses a quartile approach; this creates four categories which can readily be interpreted as two categories consisting of the areas below the median and two areas above the median. Figure 8d shows a quintile classification approach. Reclassification can be especially useful as a visualization technique; notice in figure 8 how certain areas are recognizable as having a high poverty rate regardless of the classification scheme.
Geodatabase is the term used to describe the database that contains the information relevant to a specific spatial analysis or application [ 33 ]. A geodatabase is scalable data architecture that allows the storage of all aspects of the geographic application in a relational database format.
This approach allows for greater portability and sharing of specific projects or applications while facilitating complex queries. A geodatabase integrates the GIS application software e.
This collaborative project constitutes a data-driven decision-making approach using small area risk factors, where spatial forms of data are used to assess phenomena across space and to make informed decisions about the most appropriate individual and system-wide responses.
These small area risk factors are used to take advantage of available sources of information to improve the planning, provision, and impact of services at the local and county level. This study illustrates many of the main capabilities of GIS; in this case, GIS was used to facilitate a geographic-based needs assessment, a spatial cluster analysis, and to show that the high-risk areas also overlap with the spatial clusters of individual drug users.
It is impossible or impractical to measure specific outcomes in an entire population. However, information is available on factors associated with the phenomenon, such as economic deprivation, crime, and community disorganization in the case of substance use.
Rather than trying to measure all of the specific behavioral outcomes that are of interest, such as early drinking and adolescent drug use, social indicators provide a more economical and efficient way to assess the well-being of different populations and sub-populations of interest. The use of indicators is an indirect method of needs assessment for services as it shows the relative need against the other locations in the vicinity and can help to estimate the actual need for service in some situations [ 34 ].
While risk factors and social indicators are particularly convenient sources of information for researchers and policy makers since they often can be created based on publically available data, they are also effective in providing organizations local and regional governments and service providers with information about local problems on which to focus such as poverty, alcohol availability, and crime in addition to the specific behavioral outcome of interest.
This information can be used to tailor services to specific characteristics of the population so as to enhance the effectiveness of interventions. The indicators provided in the examples here are drawn from the Erie County Risk Indicator Database RIDB and are based on the risk and protective factor model of substance abuse, delinquency, and other problem behaviors developed by Hawkins and Catalano [ 34 ].
Quartiles are used to quantify the level of risk, which is a way of assessing need for services, because these analyses are focused on comparing the risk of small areas relative to one another, as well as for their interpretability i. An important component of the development of this database of risk factors was the validation of the indicators.
This validation was carried out to address the question: Individual-level, alcohol and drug use and associated health outcome data from the Erie County Health Outcomes ECHO survey were used to assess the relationship between these indicators and the outcomes. Many significant associations between the risk indicators and the behaviors of individuals from the same geographic areas were found, supporting the notion that the risk indicators are a valid measure of the need for prevention and treatment services.
Figure 9 shows crime rate by zip code area, figure 10 shows the trauma death rate by quartile, and figure 11 shows a composite poverty index by quartile. These maps indicate that the need for alcohol and drug prevention and treatment is not evenly or randomly distributed, as well as that each indicator shows a different aspect of the need for services. Also note that these maps clearly show a number of key aspects such as overlays of major road, municipal boundaries, and the inclusion of a scale bar and a compass rose to help orient the end user.
Each map also provides an inset view of the city of Buffalo so that the details of the main urban area can be easily viewed. The general population sample of 3, total respondents aged to years old from Erie County was gathered using a random-digit-dial procedure during — The sampling frame consisted of all working telephone blocks in Erie County, New York and reflects the varying population densities throughout the county and is highly representative of the underlying population, which can be seen by comparing key figures such as race white: These comparisons indicate a minimum of non-response bias in the sample.
A total of 3, interviews were completed based on 5, eligible respondents, yielding a response rate of Of the completed interviews, were removed from the dataset because the home addresses given by the respondents was unable to be geocoded. Additionally, for these analyses, lifetime abstainers of alcohol were removed from the dataset in order to better reflect an at-risk population of controls from which our cases were generated.
Lifetime abstainers of alcohol are at substantially lower risk for developing illicit drug use problems when compared to individuals who have ever consumed alcohol. A total of respondents who were lifetime alcohol abstainers were removed from the dataset, bringing the sample size to 3, Survey respondents were questioned on usage of any illicit drug and in turn on usage of specific types of drugs cocaine, heroin, marijuana, LSD, etc. Respondents who had used an illicit drug within the past twelve months were classified as current users of a drug.
The marijuana use variable was analyzed directly, whereas all other drug use categories e. Statistically significant spatial clusters can be defined as geographically bounded groups of events where the actual number of events exceeds the expected number when compared to a distribution such as Poisson or Bernoulli.
This method has been used to examine breast cancer rates at the county level [ 38 ], alcohol mortality at the county level [ 39 ], and West Nile Virus activity [ 40 ].
When the window encounters a new case, elevated risk is tested with the likelihood function on the events within the window compared to those outside, which allows both high and low clusters to be detected.
The following equation is the likelihood function I for the Bernoulli model used in this research:. The detected clusters are then tested against a simulated Monte Carlo distribution of the data set generated under the null hypothesis. This method allows multiple clusters of both high and low use to be simultaneously detected [ 41 ]. Secondary clusters have overestimates of their true p -values because they are compared to the most likely clusters from the simulations [ 42 ].
This method is especially valuable due to its ease of use particularly in combination with GIS , applicability to both point and area data, controls for multiple comparisons and population density, and incorporation of covariate and temporal analysis which can aid in its real-world implementation for surveillance of drug-related health problems and service assessments.
Spatial cluster analysis incorporated with the use of GIS mapping capabilities offers a wealth of potential applications in research of illicit drug-related phenomena in both searching for and analyzing identified clusters. Spatial data of possible correlates or causes can be incorporated with detected clusters in GIS, but issues such as latency in exposure, migration and activity space of individuals within a population, and the differing influences of direct and mediated effects of environmental and social factors obfuscate the understanding of clustering processes and remain stumbling blocks for the development of more sophisticated and powerful theories and methods.
SaTScan v4. The user can specify the grid of coordinates used by the scanning window, frequently polygon centroids when using area data, as well as the maximum size of the scanning window as a percentage of the study population and the number of simulation iterations for the generated distribution.
In this case, the analysis takes an object-oriented approach using the coordinates of the underlying population as centers for the moving circles and uses the default settings of a 50 percent scanning window and iterations.
The default scanning window settings is the maximum window size and allows for smaller clusters to be detected as well as the largest possible clusters. A higher number of iterations serves to increase the accuracy of resultant p -values, but also takes more time, whereas fewer simulations yield slightly more uncertain p -values. This method can be computationally challenging in terms of time needed to run the cluster analysis for a personal computer, which was the platform used for these analyses.
This analysis takes advantage of the overlay capabilities of these programs, allowing the user to layer multiple sources of spatial data on the clustered populations, risk indicators, street network, municipal boundaries, and other pertinent information. Using the spatial output database of clustered and unclustered populations, appropriate statistical analysis such as cross-tabulations, mean comparisons and ANOVA can be utilized to compare clustered and unclustered population groups.
Significant spatial clusters were found for this case study for both marijuana use and for hard drug use. The clusters were not the same for the marijuana use figure 12 and hard drug use group figure 13 , although there was some overlap in high use cluster members.
The high use clusters for both drug categorizations are centered in the city of Buffalo, though the hard drug use cluster is smaller and focused in the western and north central part of the city.
The marijuana high use cluster extends slightly beyond the city boundary. Additionally, the detected cluster of low use of marijuana extends across the southeastern part of the county, containing an area that is primarily suburban and rural in character; no low hard drug use cluster was detected, suggesting that usage patterns are similar throughout the county outside of the urban high use cluster.
The analyses discussed here are primarily focused on examining the relationship between the risk indicators and clusters of substance use by comparing these data sets. These analyses assess: The tables show the proportion of each clustered drug user group that lives within the highest risk quartile for all of the RIDB indicators.
Characteristics for Marijuana Use Clusters: Characteristics for Hard Drug Use Clusters: Comparing these unrelated data sources i. Tremendously higher proportions of the high use drug clusters live in the highest need areas identified by fourteen different social indicators. Note that the lowest proportion of persons in the high use clusters, although significantly higher than for the unclustered and low use groups, are for indicators likely to be more relevant to alcohol-specific problems such as trauma deaths, suicides, and alcohol availability.
Service agencies and government planners can use this information to develop interventions at the individual and neighborhood levels that aim to address the health-related behaviors, outcomes, and specific risk factors e. This case study illustrated many of the key capabilities of a GIS when used to conduct a spatial analysis.
The case study utilized various archival data sources, multiple base maps for such aspects as boundaries, roads and geocoding, created a distance matrix for use in the clustering software, and multiple overlays were used to show the clusters over risk indicator maps. This example showed how GIS used for spatial analysis is relevant for research e. Geographic information systems GIS have developed from relatively limited access, dedicated applications in the s into the current, broadly based computerized systems designed to facilitate spatial analysis.
GIS capabilities have grown, while costs have decreased, because of the revolution toward personal computing and the development of crucial supporting software and digital base map resources, such as TIGER. Accompanying these GIS-specific developments has been the development of spatial statistics, which are key to enhancing the modeling and research capabilities of GIS.
The ability to utilize GIS for georeferencing, mapping, reclassification, distance and adjacency measures, and other related tasks has made spatial analysis feasible for simple users as well as for complex applications in spatial and network-based modeling. Despite these major advances, the spatial statistical capability of typical GIS software is relatively limited. For example, the case study showed that the GIS provided the database on distance associations needed for the cluster analysis, but other software was necessary to conduct the actual clustering.
The interface between the GIS user and between GIS and the modeling software has improved greatly since its inception; however, there is substantial progress to be made in these areas. Nonetheless, GIS has proven over the past few decades to be an indispensible tool for almost an unlimited number of practical and research applications. Griffith, D. An equation by any other name is still the same: On spatial econometrics and spatial statistics.
Ann Reg Sci. Hill, L.
The Geographic Associations of Information. Maguire, D. Obermeyer, N. Managing Geographic Information Systems. Second edition. Guilford Press, New York. National Center for Biotechnology Information , U. Comput Stat. Author manuscript; available in PMC Aug William F. Wieczorek and Alan M. Copyright and License information Disclaimer. Copyright notice. See other articles in PMC that cite the published article. Abstract This chapter presents an overview of the development, capabilities, and utilization of geographic information systems GIS.
Geographic Information Systems: Background and Basics A comprehensive history of the development of geographic information systems is not currently available and is recognized as a difficult task because GIS developed along a number of parallel paths [ 2 ]; however, some major milestones are clearly recognizable.
Here are some examples GIS definitions: Critical Aspects of Geographic Data Geography is crucial because almost every activity, feature, or decision has a geographic component. Spatial Dependence GIS, like any tool, can be a boon or a bane depending on the relevance of the application. Geographic Data Elements Although GIS can utilize both raster and vector information, raster data is not truly geographic because it is just a simple array of values.
Open in a separate window. Figure 1. Georeferencing Georeferencing, also called geocoding, is the ability to specify the location of geographic data.
Figure 2. Adjacency and Distance One main capability of a GIS is to measure distances between objects and to identify whether objects are adjacent to one another. Figure 3. Figure 4. Overlays and Queries Essential to the use of GIS is the ability to overlay multiple layers of information and access these various layers simultaneously.
Figure 5. Spatial Buffers A spatial buffer identifies a specified area around a specific geographic feature. Figure 6. Figure 7. Reclassification GIS provides the capability of reclassifying data in an automated manner. Figure 8. Geodatabase Geodatabase is the term used to describe the database that contains the information relevant to a specific spatial analysis or application [ 33 ]. Using Social Indicators It is impossible or impractical to measure specific outcomes in an entire population.
Figure 9. Figure The following equation is the likelihood function I for the Bernoulli model used in this research: Results and Discussion Significant spatial clusters were found for this case study for both marijuana use and for hard drug use. Table 1 Characteristics for Marijuana Use Clusters: Conclusion Geographic information systems GIS have developed from relatively limited access, dedicated applications in the s into the current, broadly based computerized systems designed to facilitate spatial analysis.
Further Reading Griffith, D. References 1. Mark DM. Geographic information science: Defining the field. Foundations of Geographic Information Science.
Taylor Francis; New York: Pickles J. Arguments, debates and dialogues: The GIS-social theory debate and concerns for alternatives. Geographical Information Systems: Principles, Techniques, Management, and Applications. Wiley; New York: GIS World. Roger Tomlinson: The father of GIS.
GIS World; Apr, Interview; pp. Tomlinson R. Geographic information systems — a new frontier. Peuquet DJ, Francis D, editors. Introductory Readings in Geographic Information Systems. Chrisman NR. Charting the Unknown: Washington, D. Ann Arbor, MI: History [Internet] Redlands, CA: Available from: Kidder T. The Soul of a New Machine. Back Bay Books; Boston: Marx RW. Cartography and Geographic Information Systems. New modeling methods. Alcohol Health Research World. Wieczorek WF.
Using geographic information systems for small area analysis. NIH Pub.
Concepts and Techniques of Geographic Information Systems. Prentice Hall: Upper Saddle River; Tait M. Implementing geoportals: Computers, Environment and Urban Systems. Informing geospatial toolset design: Natural conversational interfaces to geospatial databases. Transactions in GIS. Geographic Information Systems and Science.
Burrough PA. Monographs on Soil and Resources Survey No. Oxford Science Publications; New York: Understanding GIS: