4: Attribute Tables and Data Preprocessing
- Page ID
- 44910
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- 4.1: Introduction
- This page focuses on attribute data files in GIS, building on concepts from the previous chapter about digital maps. It covers essential principles of both raster and vector database management, detailing preprocessing routines like adding/deleting fields, joining data files, and geocoding. The chapter concludes with a discussion on attribute verification, highlighting the importance of effective editing and management of GIS attribute data.
- 4.2: Attribute Data
- This page discusses non-spatial attributes of GIS files, categorizing them into qualitative and quantitative types. Quantitative data is measurable and analyzable, whereas qualitative data offers subjective descriptions. Attributes are also classified as absolute (natural measurements) and derived (resulting from statistical manipulation).
- 4.3: Principles Of Database Management – Vector
- This page explains data organization in geographic information systems (GIS), focusing on data files (tables) that hold attributes of geographic features. Each record corresponds to a feature, with fields denoting specific attributes. The relational database model is predominant in GIS for linking records via key identifiers, despite challenges in representing spatial data. It remains favored for its simplicity compared to more complex object-oriented models.
- 4.4: Principles Of Database Management – Raster
- This page discusses the raster data model, which organizes the Earth's surface into a grid of pixels, each with a value, including empty areas marked as "0." It allows for multiple layers showcasing different attributes. Data storage methods include regular formats and run-length encoding for efficiency, while irregular structures like quadtrees offer size reduction and improved data retrieval but with increased complexity.
- 4.5: Attribute Preprocessing And Editing
- This page outlines attribute data management in GIS, detailing processes like adding, deleting, joining, and sorting data files. It explains how to open attribute tables and integrate external data through key identifiers while discussing compatibility of various file formats. Additionally, it covers attribute queries using set algebra and Boolean logic, methods for calculating new attributes, geocoding, and address matching to create spatial layers.
- 4.6: Attribute Verification
- This page explains the verification of attribute data accuracy, emphasizing the identification of missing attributes and incorrect values. Unlike geographic verification, this process lacks automation and involves manual data sorting to spot issues. Detecting incorrect values is more complex, necessitating familiarity with source maps and spatial patterns. Analyzing income data or land use classes relative to geographic locations aids in validating accuracy.
Thumbnail: white and green state maps. (Unsplash License; Annie Spratt via Unsplash)