4.2: Attribute Data
- Page ID
- 44912
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)As described in the previous chapter, spatial data occupies geographic space. It has a specific location that is tied to one of the world’s geographic referencing systems (like latitude and longitude). Besides spatial data, GIS files contain non-spatial attributes that describe the spatial features. This section focuses on these non-spatial attributes.
Related to the discussion of “measurements of scale” in Chapter 2, your attributes can be classified as either qualitative or quantitative and actual or derived. Quantitative data focus on numbers and frequencies rather than on subjectivity, meaning, and experience. They are easy to analyze statistically, and their values are often the result of field work and laboratory experiments. Maps exhibiting quantitative data depict differences in magnitude among features.
Qualitative data, by contrast, often provide deeper description and meaning. Maps displaying qualitative data show differences in kind or type. You might subjectively judge whether a quantity is low, medium, or high. You might also classify detailed land uses into broader categories of residential, commercial, and industrial. The statistical options are narrowed too due to the subjectivity of the data and the categorization of data into classes.
Data can also be defined by whether they represent some intrinsic characteristic of the feature being measured (absolute), or whether they are in a sense “created” (derived). Absolute data consists of both the quantitative and qualitative data just described, but it represents phenomena that are measured (like election data or the amount of water stored), the ranking and rating of attributes (even though this process can be subjective), and personal, subjective accounts gained from questionnaires and surveys.
Derived attributes either do not occur naturally, or they cannot be directly gathered; they are the result of statistical manipulation that produces the data. An example is average July temperatures, which is the calculated result of averaging many actual temperature values. Derived data may result from averaging actual values like these, or they represent the relationships between already gathered attribute data, which take three forms: ratio, proportion, and percentage.
- Ratio attributes are derived when the value of one attribute is divided by the value of another. Population density is a good example. The total number of people within a particular region is divided by the region’s area. Both the population and area attributes may be “actual” values, but the calculated population density attribute is derived.
- Proportion compares the value of one attribute to the total value of all related attributes. The proportion of all African-Americans to the total population is derived by dividing the number of African-Americans (actual data) by the total number of people (also actual).
- Many people think of proportions as percentages; they are similar, but percentages multiply proportions by one hundred.