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A Geographic Information System is a multi-component environment used to create, manage, visualize and analyze data and its spatial counterpart. If, for example, we are interested in identifying the ten African countries with the highest conflict index scores for the period, a simple table listing those scores by country is all that is needed.
Data source: Anselin, L. Geography of international conflict and cooperation: spatial dependence and regional context in Africa. In The New Geopolitics, ed. Ward, pp. What if we are interested in knowing whether countries with a high conflict index score are geographically clustered, does the above table provide us with enough information to help answer this question? The answer, of course, is no. We need additional data pertaining to the geographic location and shape of each country.
A map of the countries would be helpful. Figure 1. Countries for which a score was not available are not mapped. Maps are ubiquitous: available online and in various print medium. But we seldom ask how the boundaries of the map features are encoded in a computing environment? After all, if we expect software to assist us in the analysis, the spatial elements of our data should be readily accessible in a digital form.
Spending a few minutes thinking through this question will make you realize that simple tables or spreadsheets are not up to this task. A more complex data storage mechanism is required.
This is the core of a GIS environment: a spatial database that facilitates the storage and retrieval of data that define the spatial boundaries, lines or points of the entities we are studying. This may seem trivial, but without a spatial database, most spatial data exploration and analysis would not be possible!
Many GIS software applications are available—both commercial and open source. ArcGIS comes in three different license levels basic, standard and advanced and can be purchased with additional add-on packages. As such, a single license can range from a few thousand dollars to well over ten thousand dollars. In addition to software licensing costs, ArcGIS is only available for Windows operating systems; so if your workplace is a Mac only environment, the purchase of a Windows PC would add to the expense.
It encompasses most of the functionality included in ArcGIS. A distinction is made in this course between GIS and spatial analysis. In the context of mainstream GIS software, the term analysis refers to data manipulation and data querying.
For example, you record the location of each tree in a well defined study area. You then map the location of each tree a GIS task. At this point, you might be inclined to make inferences about the observed pattern. Are the trees clustered or dispersed? Is the tree density constant across the study area? Could soil type or slope have led to the observed pattern? Those are questions that are addressed in spatial analysis using quantitative and statistical techniques.
What you will learn in this course is that popular GIS software like ArcGIS are great tools to create and manipulate spatial data, but if one wishes to go beyond the data manipulation and analyze patterns and processes that may have led to these patterns, other quantitative tools are needed. One such tool we will use in this class is R : an open source freeware data analysis environment.
R has one, if not the richest set of spatial data analysis and statistics tools available today. Learning the R programming environment will prove to be quite beneficial given that many of the operations learnt are transferable across many other non-spatial quantitative analysis projects. R can be installed on both Windows and Mac operating systems. Another related piece of software that you might find useful is RStudio which offers a nice interface to R.
To learn more about data analysis in R, visit the ES course website. GIS is a ubiquitous technology. We can expand on this idea and define accidental data analyst as one whose understanding of data and its analysis is limited to the point-and-click environment of popular pieces of software such as spreadsheet environments, statistical packages and GIS software. The aggressive marketing of GIS technology has the undesirable effect of placing the technology before purpose and theory.
This is not unique to GIS, however. Such concerns were shared decades ago when personal computers made it easier for researchers and employees to graph non-spatial data as well as perform many statistical procedures. The different purposes of mapping spatial data have strong parallels to that of graphing or plotting non-spatial data. John Tukey Tukey offers three broad classes of the latter:. This course will focus on the last two spatial data visualization purposes with a strong emphasis on the latter Statistical maps.
Preface 1 Introduction to GIS 1. Raster 2. Field 2. Chapter 1 Introduction to GIS 1. Table 1. John Tukey Tukey offers three broad classes of the latter: " Graphs from which numbers are to be read off- substitutes for tables. Graphs intended to show the reader what has already been learned by some other technique —these we shall sometimes impolitely call propaganda graphs. Graphs intended to let us see what may be happening over and above what we have already described- these are the analytical graphs that are our main topic.
Such maps are used to navigate landscapes or identify locations of points-of-interest. Presentation maps presented in the press such as the NY Times and the Wall Street Journal, but also maps presented in journals.
Statistical maps whose purpose it is to manipulate the raw data in such a way to tease out patterns otherwise not discernable in its original form. This usually requires multiple data manipulation operations and visualization and can sometimes benefit from being explored outside of a spatial context. Geographic Information Analysis. Tukey, John W. Snedecor , edited by T. Bancroft, — August
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