{"id":40,"date":"2014-01-23T22:10:51","date_gmt":"2014-01-23T22:10:51","guid":{"rendered":"http:\/\/opentextbc.ca\/natureofgeographicinformation\/?post_type=chapter&p=40"},"modified":"2016-12-09T20:47:30","modified_gmt":"2016-12-09T20:47:30","slug":"1-overview","status":"publish","type":"chapter","link":"https:\/\/opentextbc.ca\/natureofgeographicinformation\/chapter\/1-overview\/","title":{"raw":"Data and Information","rendered":"Data and Information"},"content":{"raw":"

1.1. Overview<\/h2>\r\nWhen I started writing this text in 1997, my office was across the street (and, fortunately, upwind) from Penn State's power plant. The energy used to heat and cool my office is still produced there by burning coal mined from nearby ridges. Combustion transforms the potential energy stored in the coal into electricity, which solves the problem of an office that would otherwise be too cold or too warm. Unfortunately, the solution itself causes another problem, namely emissions of carbon dioxide and other more noxious substances into the atmosphere. Cleaner means of generating electricity exist, of course, but they too involve transforming energy from one form to another. And cleaner methods cost more than most of us are willing or able to pay.\r\n\r\nIt seems to me that a coal-fired power plant is a pretty good analogy for a geographic information system. For that matter, GIS is comparable to any factory or machine that transforms a raw material into something more valuable. Data is grist for the GIS mill. GIS is like the machinery that transforms the data into the commodity--information--that is needed to solve problems or create opportunities. And the problems that the manufacturing process itself creates include uncertainties resulting from imperfections in the data, intentional or unintentional misuse of the machinery, and ethical issues related to what the information is used for, and who has access to it.\r\n\r\nThis text explores the nature of geographic information. To study the nature of something is to investigate its essential characteristics and qualities. To understand the nature of the energy produced in a coal-fired power plant, one should study the properties, morphology, and geographic distribution of coal. By the same reasoning I believe that a good approach to understanding the information produced by GIS is to investigate the properties of geographic data and the technologies and institutions that produce it.\r\n

Objectives<\/h3>\r\nThe goal of Chapter 1 is to situate GIS in a larger enterprise known as Geographic Information Science and Technology (GIS&T), and in what the U.S. Department of Labor calls the \"geospatial industry.\" In particular, students who successfully complete Chapter 1 should be able to:\r\n
    \r\n \t
  1. Define a geographic information system;<\/li>\r\n \t
  2. Recognize and name basic database operations from verbal descriptions;<\/li>\r\n \t
  3. Recognize and name basic approaches to geographic representation from verbal descriptions;<\/li>\r\n \t
  4. Identify and explain at least three distinguishing properties of geographic data; and<\/li>\r\n \t
  5. Outline the kinds of questions that GIS can help answer.<\/li>\r\n<\/ol>\r\n

    1.2. Checklist<\/h2>\r\nThe following checklist is for Penn State students who are registered for classes in which this text, and associated quizzes and projects in the ANGEL course management system, have been assigned. You may find it useful to print this page out first so that you can follow along with the directions.\r\n

    Chapter 1 Checklist (for registered students only)<\/h3>\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    Chapter 1 Checklist<\/caption>\r\n
    Step<\/th>\r\nActivity<\/th>\r\nAccess\/Directions<\/th>\r\n<\/tr>\r\n
    1<\/th>\r\nRead<\/strong>\u00a0Chapter 1<\/td>\r\nThis is the second page of Chapter 1. Click on the links at the bottom of the page to continue or to return to the previous page, or to go to the top of the chapter. You can also navigate the text via the links in the GEOG 482 menu on the left.<\/td>\r\n<\/tr>\r\n
    2<\/th>\r\nSubmit\u00a0quizzes<\/strong>\u00a0as you come across them in the chapter. Blue banners denote practice quizzes that are not graded. Red banners signal graded quizzes. (Note that Chapter 1 does not include a graded quiz.)<\/td>\r\nGo to ANGEL > [your course section] > Lessons tab > Chapter 1 folder > [quiz]<\/td>\r\n<\/tr>\r\n
    3<\/th>\r\nPerform\u00a0\"Try This\" activities<\/strong>\u00a0as you come across them in the chapter. \"Try This\" activities are not graded.<\/td>\r\nInstructions are provided for each activity.<\/td>\r\n<\/tr>\r\n
    4<\/th>\r\n\u00a0Read\u00a0comments and questions<\/strong>\u00a0posted by fellow students. Add comments and questions of your own, if any.<\/td>\r\n\u00a0Comments and questions may be posted on any page of the text, or in a Chapter-specific discussion forum in ANGEL.<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

    \u00a01.3. Data<\/h2>\r\n\"After more than 30 years, we're still confronted by the same major challenge that GIS professionals have always faced: You must have good data. And good data are expensive and difficult to create.\" (Wilson, 2001, p. 54)\r\n\r\nData consist of symbols that represent measurements of phenomena.\u00a0<\/strong>People create and study data as a means to help understand how natural and social systems work. Such systems can be hard to study because they're made up of many interacting phenomena that are often difficult to observe directly, and because they tend to change over time. We attempt to make systems and phenomena easier to study by measuring their characteristics at certain times. Because it's not practical to measure everything, everywhere, at all times, we measure selectively. How accurately data reflect the phenomena they represent depends on how, when, where, and what aspects of the phenomena were measured. All measurements, however, contain a certain amount of error.\r\n\r\nMeasurements of the locations and characteristics of phenomena can be represented with several different kinds of symbols. For example, pictures of the land surface, including photographs and maps, are made up of graphic symbols. Verbal descriptions of property boundaries are recorded on deeds using alphanumeric symbols. Locations determined by satellite positioning systems are reported as pairs of numbers called coordinates. As you probably know, all of these different types of data--pictures, words, and numbers--can be represented in computers in digital form. Obviously, digital data can be stored, transmitted, and processed much more efficiently than their physical counterparts that are printed on paper. These advantages set the stage for the development and widespread adoption of GIS.\r\n

    1.4. Information<\/h2>\r\nInformation is data that has been selected or created in response to a question.<\/strong>\u00a0For example, the location of a building or a route is data, until they are needed to dispatch an ambulance in response to an emergency. When used to inform those who need to know \"where is the emergency, and what's the fastest route between here and there?,\" the data are transformed into information. The transformation involves the ability to ask the right kind of question, and the ability to retrieve existing data--or to generate new data from the old--that help people answer the question. The more complex the question, and the more locations involved, the harder it becomes to produce timely information with paper maps alone.\r\n\r\nInterestingly, the potential value of data is not necessarily lost when they are used. Data can be transformed into information again and again, provided that the data are kept up to date. Given the rapidly increasing accessibility of computers and communications networks in the U.S. and abroad, it's not surprising that information has become a commodity, and that the ability to produce it has become a major growth industry.\r\n

    1.5. Information Systems<\/h2>\r\nInformation systems are computer-based tools that help people transform data into information.<\/strong>\r\n\r\nAs you know, many of the problems and opportunities faced by government agencies, businesses, and other organizations are so complex, and involve so many locations, that the organizations need assistance in creating useful and timely information. That\u2019s what information systems are for.\r\n\r\nAllow me a fanciful example. Suppose that you\u2019ve launched a new business that manufactures solar-powered lawn mowers. You\u2019re planning a direct mail campaign to bring this revolutionary new product to the attention of prospective buyers. But since it\u2019s a small business, you can\u2019t afford to sponsor coast-to-coast television commercials, or to send brochures by mail to more than 100 million U.S. households. Instead, you plan to target the most likely customers \u2013 those who are environmentally conscious, have higher than average family incomes, and who live in areas where there is enough water and sunshine to support lawns and solar power.\r\n\r\nFortunately, lots of data are available to help you define your mailing list. Household incomes are routinely reported to banks and other financial institutions when families apply for mortgages, loans, and credit cards. Personal tastes related to issues like the environment are reflected in behaviors such as magazine subscriptions and credit card purchases. Firms like Claritas amass such data, and transform it into information by creating \u201clifestyle segments\u201d \u2013 categories of households that have similar incomes and tastes. Your solar lawnmower company can purchase lifestyle segment information by 5-digit ZIP code, or even by ZIP+4 codes, which designate individual households.\r\n\r\nIt\u2019s astonishing how companies like Claritas can create valuable information from the millions upon millions of transactions that are recorded every day. Their products are made possible by the fact that the original data exist in digital form, and because the companies have developed information systems that enable them to transform the data into information that companies like yours value. The fact that lifestyle information products are often delivered by geographic areas, such as ZIP codes, speaks to the appeal of geographic information systems.\r\n

    TRY THIS<\/strong><\/h3>\r\nTry out the demo of what Claritas used to call the \u201cYou Are Where You Live\u201d tool. The Nielson Company has acquired Claritas and the tool is now called \u201cMyBestSegments.\u201d Point your browser to the\u00a0My Best Segments<\/a>\u00a0page. Click the button labeled \u201cZIP Code Look-up.\u201d\r\n\r\nEnter your ZIP code then choose a segmentation system. Do the lifestyle segments, listed on the left, seem accurate for your community? If you don\u2019t live in the United States, try Penn State\u2019s Zip code, 16802.\r\nDoes the market segmentation match your expectations? Registered students are welcome to post comments directly to this page.\r\n

    1.6. Databases, Mapping, and GIS<\/h2>\r\nOne of our objectives in this first chapter is to be able to define a geographic information system. Here\u2019s a tentative definition:\u00a0A GIS is a computer-based tool used to help people transform geographic data into geographic information.<\/strong>\r\n\r\nThe definition implies that a GIS is somehow different from other information systems, and that geographic data are different from non-geographic data. Let\u2019s consider the differences next.\r\n

    1.7. Database Management Systems<\/h2>\r\nClaritas and similar companies use database management systems (DBMS) to create the \u201clifestyle segments\u201d that I referred to in the previous section. Basic database concepts are important since GIS incorporates much of the functionality of DBMS.\r\n\r\nDigital data are stored in computers as files. Often, data are arrayed in tabular form. For this reason, data files are often called\u00a0tables<\/strong>. A\u00a0database<\/strong>\u00a0is a collection of tables. Businesses and government agencies that serve large clienteles, such as telecommunications companies, airlines, credit card firms, and banks, rely on extensive databases for their billing, payroll, inventory, and marketing operations.\u00a0Database management systems<\/strong>\u00a0are information systems that people use to store, update, and analyze non-geographic databases.\r\n\r\nOften, data files are tabular in form, composed of rows and columns.\u00a0Rows<\/strong>, also known as\u00a0records<\/strong>, correspond with individual entities, such as customer accounts.\u00a0Columns<\/strong>\u00a0correspond with the variousattributes<\/strong>\u00a0associated with each entity. The attributes stored in the accounts database of a telecommunications company, for example, might include customer names, telephone numbers, addresses, current charges for local calls, long distance calls, taxes, etc.\r\n\r\nGeographic data<\/strong>\u00a0are a special case: records correspond with places, not people or accounts. Columns represent the attributes of places. The data in the following table, for example, consist of records for Pennsylvania counties. Columns contain selected attributes of each county, including the county\u2019s ID code, name, and 1980 population.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    1980 Population Data for PA Counties<\/caption>\r\n
    FIPS Code<\/th>\r\nCounty<\/th>\r\n1980 Pop<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
    42001<\/td>\r\nAdams County<\/td>\r\n78274<\/td>\r\n<\/tr>\r\n
    42003<\/td>\r\nAllegheny County<\/td>\r\n1336449<\/td>\r\n<\/tr>\r\n
    42005<\/td>\r\nArmstrong County<\/td>\r\n73478<\/td>\r\n<\/tr>\r\n
    42007<\/td>\r\nBeaver County<\/td>\r\n186093<\/td>\r\n<\/tr>\r\n
    42009<\/td>\r\nBedford County<\/td>\r\n47919<\/td>\r\n<\/tr>\r\n
    42011<\/td>\r\nBerks County<\/td>\r\n336523<\/td>\r\n<\/tr>\r\n
    42013<\/td>\r\nBlair County<\/td>\r\n130542<\/td>\r\n<\/tr>\r\n
    42015<\/td>\r\nBradford County<\/td>\r\n60967<\/td>\r\n<\/tr>\r\n
    42017<\/td>\r\nBucks County<\/td>\r\n541174<\/td>\r\n<\/tr>\r\n
    42019<\/td>\r\nButler County<\/td>\r\n152013<\/td>\r\n<\/tr>\r\n
    42021<\/td>\r\nCambria County<\/td>\r\n163062<\/td>\r\n<\/tr>\r\n
    42023<\/td>\r\nCameron County<\/td>\r\n5913<\/td>\r\n<\/tr>\r\n
    42025<\/td>\r\nCarbon County<\/td>\r\n56846<\/td>\r\n<\/tr>\r\n
    42027<\/td>\r\nCentre County<\/td>\r\n124812<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nThe contents of one file in a database.\r\n\r\nThe example is a very simple file, but many geographic attribute databases are in fact very large (the U.S. is made up of over 3,000 counties, almost 50,000 census tracts, about 43,000 five-digit ZIP code areas and many tens of thousands more ZIP+4 code areas). Large databases consist not only of lots of data, but also lots of files. Unlike a spreadsheet, which performs calculations only on data that are present in a single document, database management systems allow users to store data in, and retrieve data from, many separate files. For example, suppose an analyst wished to calculate population change for Pennsylvania counties between the 1980 and 1990 censuses. More than likely, 1990 population data would exist in a separate file, like so:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    1990 Population Data for PA Counties<\/caption>\r\n
    FIPS Code<\/th>\r\n1990 Pop<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
    42001<\/td>\r\n84921<\/td>\r\n<\/tr>\r\n
    42003<\/td>\r\n1296037<\/td>\r\n<\/tr>\r\n
    42005<\/td>\r\n73872<\/td>\r\n<\/tr>\r\n
    42007<\/td>\r\n187009<\/td>\r\n<\/tr>\r\n
    42009<\/td>\r\n49322<\/td>\r\n<\/tr>\r\n
    42011<\/td>\r\n352353<\/td>\r\n<\/tr>\r\n
    42013<\/td>\r\n131450<\/td>\r\n<\/tr>\r\n
    42015<\/td>\r\n62352<\/td>\r\n<\/tr>\r\n
    42017<\/td>\r\n578715<\/td>\r\n<\/tr>\r\n
    42019<\/td>\r\n167732<\/td>\r\n<\/tr>\r\n
    42021<\/td>\r\n158500<\/td>\r\n<\/tr>\r\n
    42023<\/td>\r\n5745<\/td>\r\n<\/tr>\r\n
    42025<\/td>\r\n58783<\/td>\r\n<\/tr>\r\n
    42027<\/td>\r\n131489<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nAnother file in a database. A database management system (DBMS) can relate this file to the prior one illustrated above because they share the list of attributes called \u201cFIPS Code.\u201d\r\n\r\nIf two data files have at least one common attribute, a DBMS can combine them in a single new file. The common attribute is called a\u00a0key<\/strong>. In this example, the key was the county FIPS code (FIPS stands for Federal Information Processing Standard). The DBMS allows users to produce new data as well as to retrieve existing data, as suggested by the new \u201c% Change\u201d attribute in the table below.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    Percent Change in Populations for PA Counties 1980-1990<\/caption>\r\n
    FIPS<\/th>\r\nCounty<\/th>\r\n1980<\/th>\r\n1990<\/th>\r\n% Change<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
    42001<\/td>\r\nAdams<\/td>\r\n78274<\/td>\r\n84921<\/td>\r\n8.5<\/td>\r\n<\/tr>\r\n
    42003<\/td>\r\nAllegheny<\/td>\r\n1336449<\/td>\r\n1296037<\/td>\r\n-3<\/td>\r\n<\/tr>\r\n
    42005<\/td>\r\nArmstrong<\/td>\r\n73478<\/td>\r\n73872<\/td>\r\n0.5<\/td>\r\n<\/tr>\r\n
    42007<\/td>\r\nBeaver<\/td>\r\n186093<\/td>\r\n187009<\/td>\r\n0.5<\/td>\r\n<\/tr>\r\n
    42009<\/td>\r\nBedford<\/td>\r\n47919<\/td>\r\n49322<\/td>\r\n2.9<\/td>\r\n<\/tr>\r\n
    42011<\/td>\r\nBerks<\/td>\r\n336523<\/td>\r\n352353<\/td>\r\n4.7<\/td>\r\n<\/tr>\r\n
    42013<\/td>\r\nBlair<\/td>\r\n130542<\/td>\r\n131450<\/td>\r\n0.7<\/td>\r\n<\/tr>\r\n
    42015<\/td>\r\nBradford<\/td>\r\n60967<\/td>\r\n62352<\/td>\r\n2.3<\/td>\r\n<\/tr>\r\n
    42017<\/td>\r\nBucks<\/td>\r\n541174<\/td>\r\n578715<\/td>\r\n6.9<\/td>\r\n<\/tr>\r\n
    42019<\/td>\r\nButler<\/td>\r\n152013<\/td>\r\n167732<\/td>\r\n10.3<\/td>\r\n<\/tr>\r\n
    42021<\/td>\r\nCambria<\/td>\r\n163062<\/td>\r\n158500<\/td>\r\n-2.8<\/td>\r\n<\/tr>\r\n
    42023<\/td>\r\nCameron<\/td>\r\n5913<\/td>\r\n5745<\/td>\r\n-2.8<\/td>\r\n<\/tr>\r\n
    42025<\/td>\r\nCarbon<\/td>\r\n56846<\/td>\r\n58783<\/td>\r\n3.4<\/td>\r\n<\/tr>\r\n
    42027<\/td>\r\nCentre<\/td>\r\n124812<\/td>\r\n131489<\/td>\r\n5.3<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nA new file produced from the prior two files as a result of two database operations. One operation merged the contents of the two files without redundancy. A second operation produced a new attribute\u2013\u201d% Change\u201d\u2013dividing the difference between \u201c1990 Pop\u201d and \u201c1980 Pop\u201d by \u201c1980 Pop\u201d and expressing the result as a percentage.\r\n\r\nDatabase management systems are valuable because they provide secure means of storing and updating data. Database administrators can protect files so that only authorized users can make changes. DBMS provide transaction management functions that allow multiple users to edit the database simultaneously. In addition, DBMS also provide sophisticated means to retrieve data that meet user specified criteria. In other words, they enable users to select data in response to particular questions. A question that is addressed to a database through a DBMS is called a\u00a0query<\/strong>.\r\n\r\nDatabase queries include basic set operations, including union, intersection, and difference. The product of aunion<\/strong>\u00a0of two or more data files is a single file that includes all records and attributes, without redundancy. An\u00a0intersection<\/strong>\u00a0produces a data file that contains only records present in all files. A\u00a0difference<\/strong>\u00a0operation produces a data file that eliminates records that appear in both original files. (Try drawing Venn diagrams\u2013intersecting circles that show relationships between two or more entities\u2013to illustrate the three operations. Then compare your sketch to\u00a0the venn diagram example<\/a>. ) All operations that involve multiple data files rely on the fact that all files contain a common key. The key allows the database system to relate the separate files. Databases that contain numerous files that share one or more keys are called\u00a0relational databases<\/strong>. Database systems that enable users to produce information from relational databases are calledrelational database management systems<\/strong>.\r\n\r\nA common use of database queries is to identify subsets of records that meet criteria established by the user. For example, a credit card company may wish to identify all accounts that are 30 days or more past due. A county tax assessor may need to list all properties not assessed within the past 10 years. Or the U.S. Census Bureau may wish to identify all addresses that need to be visited by census takers, because census questionnaires were not returned by mail. DBMS software vendors have adopted a standardized language called SQL (Structured Query Language) to pose such queries.\r\n

    PRACTICE QUIZ<\/strong><\/h3>\r\n

    1.8. Mapping Systems<\/h2>\r\nGIS (geographic information systems) arose out of the need to perform spatial queries on geographic data. A spatial query requires knowledge of locations as well as attributes. For example, an environmental analyst might want to know which public drinking water sources are located within one mile of a known toxic chemical spill. Or, a planner might be called upon to identify property parcels located in areas that are subject to flooding. To accommodate geographic data and spatial queries, database management systems need to be integrated with mapping systems. Until about 1990, most maps were printed from handmade drawings or engravings. Geographic data produced by draftspersons consisted of graphic marks inscribed on paper or film. To this day, most of the lines that appear on topographic maps published by the U.S. Geological Survey were originally engraved by hand. The place names shown on the maps were affixed with tweezers, one word at a time. Needless to say, such maps were expensive to create and to keep up to date. Computerization of the mapmaking process had obvious appeal.\r\n\r\nComputer-aided design (CAD)<\/strong>\u00a0CAD systems were originally developed for engineers, architects, and other design professionals who needed more efficient means to create and revise precise drawings of machine parts, construction plans, and the like. In the 1980s, mapmakers began to adopt CAD in place of traditional map drafting. CAD operators encode the locations and extents of roads, streams, boundaries and other entities by tracing maps mounted on electronic drafting tables, or by key-entering location coordinates, angles, and distances. Instead of graphic features, CAD data consist of digital features, each of which is composed of a set of point locations. Calculations of distances, areas, and volumes can easily be automated once features are digitized. Unfortunately, CAD systems typically do not encode data in forms that support spatial queries. In 1988, a geographer named David Cowen illustrated the benefits and shortcomings of CAD for spatial decision making. He pointed out that a CAD system would be useful for depicting the streets, property parcel boundaries, and building footprints of a residential subdevelopment. A CAD operator could point to a particular parcel, and highlight it with a selected color or pattern. \u201cA typical CAD system\u201d, Cowen observed, \u201ccould not automatically shade each parcel based on values in an assessor\u2019s database containing information regarding ownership, usage, or value, however.\u201d A CAD system would be of limited use to someone who had to make decisions about land use policy or tax assessment.\r\n\r\nDesktop mapping\u00a0\u00a0<\/strong>An evolutionary stage in the development of GIS, desktop mapping systems like Atlas*GIS combined some of the capabilities of CAD systems with rudimentary linkages between location data and attribute data. A desktop mapping system user could produce a map in which property parcels are automatically colored according to various categories of property values, for example. Furthermore, if property value categories were redefined, the map\u2019s appearance could be updated automatically. Some desktop mapping systems even supported simple queries that allow users to retrieve records from a single attribute file. Most real-world decisions require more sophisticated queries involving multiple data files. That\u2019s where real GIS comes in.\r\n\r\nGeographic information systems (GIS)<\/strong>\u00a0As stated earlier, information systems assist decision makers by enabling them to transform data into useful information. GIS specializes in helping users transform geographic data into geographic information. David Cowen (1988) defined GIS as a decision support tool that combines the attribute data handling capabilities of relational database management systems with the spatial data handling capabilities of CAD and desktop mapping systems. In particular, GIS enables decision makers to identify locations or routes whose attributes match multiple criteria, even though entities and attributes may be encoded in many different data files.\r\n\r\nInnovators in many fields, including engineers, computer scientists, geographers, and others, started developing digital mapping and CAD systems in the 1950s and 60s. One of the first challenges they faced was to convert the graphical data stored on paper maps into digital data that could be stored in, and processed by, digital computers. Several different approaches to representing locations and extents in digital form were developed. The two predominant representation strategies are known as \u201cvector\u201d and \u201craster.\u201d\r\n

    1.9. Representation Strategies for Mapping<\/h2>\r\nRecall that data consist of symbols that represent measurements. Digital geographic data are encoded as alphanumeric symbols that represent locations and attributes of locations measured at or near Earth\u2019s surface. No geographic data set represents every possible location, of course. The Earth is too big, and the number of unique locations is too great. In much the same way that public opinion is measured through polls, geographic data are constructed by measuring representative\u00a0samples<\/strong>\u00a0of locations. And just as serious opinion polls are based on sound principles of statistical sampling, so too do geographic data represent reality by measuring carefully chosen samples of locations. Vector and raster data are, at essence, two distinct sampling strategies.\r\n\r\nThe\u00a0vector<\/strong>\u00a0approach involves sampling locations at intervals along the length of linear entities (like roads), or around the perimeter of areal entities (like property parcels). When they are connected by lines, the sampled points form line features and polygon features that approximate the shapes of their real-world counterparts.\r\n\r\n\"Illustration<\/a>\r\n\r\nTwo frames (the first and last) of an animation showing the construction of a vector representation of a reservoir and highway.\r\n

    TRY THIS<\/strong><\/h3>\r\nClick the graphic above to download and view the animation file (vector.avi, 1.6 Mb) in a separate Microsoft Media Player window.\r\n\r\nTo view the\u00a0same animation in QuickTime format (vector.mov, 1.6 Mb), click here<\/a>. Requires the QuickTime plugin, which is available free at\u00a0apple.com<\/a>.\r\n\r\nThe aerial photograph above left shows two entities, a reservoir and a highway. The graphic above right illustrates how the entities might be represented with vector data. The small squares are nodes: point locations specified by latitude and longitude coordinates. Line segments connect nodes to form line features. In this case, the line feature colored red represents the highway. Series of line segments that begin and end at the same node form polygon features. In this case, two polygons (filled with blue) represent the reservoir.\r\n\r\nThe vector data model is consistent with how surveyors measure locations at intervals as they traverse a property boundary. Computer-aided drafting (CAD) software used by surveyors, engineers, and others, stores data in vector form. CAD operators encode the locations and extents of entities by tracing maps mounted on electronic drafting tables, or by key-entering location coordinates, angles, and distances. Instead of graphic features, CAD data consist of digital features, each of which is composed of a set of point locations.\r\n\r\nThe vector strategy is well suited to mapping entities with well-defined edges, such as highways or pipelines or property parcels. Many of the features shown on paper maps, including contour lines, transportation routes, and political boundaries, can be represented effectively in digital form using the vector data model.\r\n\r\nThe\u00a0raster<\/strong>\u00a0approach involves sampling attributes at fixed intervals. Each sample represents one cell in a checkerboard-shaped grid.\r\n\r\n\"Illustration<\/a>\r\n\r\nTwo frames (the first and last) of an animation showing the construction of a raster representation of a reservoir and highway.\r\n\r\n \r\n

    TRY THIS<\/strong><\/h3>\r\nClick the graphic above to download and view the animation file (raster.avi, 0.8 Mb) in a separate Microsoft Media Player window.\r\n\r\nTo view the\u00a0same animation in QuickTime format (raster.mov, 0.6 Mb), click here<\/a>. Requires the QuickTime plugin, which is available free at\u00a0apple.com<\/a>.\r\n\r\nThe graphic above illustrates a raster representation of the same reservoir and highway as shown in the vector representation. The area covered by the aerial photograph has been divided into a grid. Every grid cell that overlaps one of the two selected entities is encoded with an attribute that associates it with the entity it represents. Actual raster data would not consist of a picture of red and blue grid cells, of course; they would consist of a list of numbers, one number for each grid cell, each number representing an entity. For example, grid cells that represent the highway might be coded with the number \u201c1\u2033 and grid cells representing the reservoir might be coded with the number \u201c2.\u201d\r\n\r\nThe raster strategy is a smart choice for representing phenomena that lack clear-cut boundaries, such as terrain elevation, vegetation, and precipitation. Digital airborne imaging systems, which are replacing photographic cameras as primary sources of detailed geographic data, produce raster data by scanning the Earth\u2019s surface pixel by pixel and row by row.\r\n\r\nBoth the vector and raster approaches accomplish the same thing: they allow us to caricature the Earth\u2019s surface with a limited number of locations. What distinguishes the two is the sampling strategies they embody. The vector approach is like creating a picture of a landscape with shards of stained glass cut to various shapes and sizes. The raster approach, by contrast, is more like creating a mosaic with tiles of uniform size. Neither is well suited to all applications, however. Several variations on the vector and raster themes are in use for specialized applications, and the development of new object-oriented approaches is underway.\r\n

    PRACTICE QUIZ<\/strong><\/h3>\r\n

    1.10. Automated Map Analysis<\/h2>\r\nAs I mentioned earlier, the original motivation for developing computer mapping systems was to automate the map making process. Computerization has not only made map making more efficient, it has also removed some of the technological barriers that used to prevent people from making maps themselves. What used to be an arcane craft practiced by a few specialists has become a \u201ccloud\u201d application available to any networked computer user. When I first started writing this course in 1997, my example was the mapping extension included in Microsoft Excel 97, which made creating a simple map as easy as creating a graph. Ten years later, who hasn\u2019t used Google Maps or MapQuest?\r\n\r\nAs much as computerization has changed the way maps are made, it has had an even greater impact on how maps can be used. Calculations of distance, direction, and area, for example, are tedious and error-prone operations with paper maps. Given a digital map, such calculations can easily be automated. Those who are familiar with CAD systems know this from first-hand experience. Highway engineers, for example, rely on aerial imagery and digital mapping systems to estimate project costs by calculating the volumes of rock that need to be excavated from hillsides and filled into valleys.\r\n\r\nThe ability to automate analytical tasks not only relieves tedium and reduces errors. It also allows us to perform tasks that would otherwise seem impractical. Consider, for example, if you were asked to plot on a map a 100-meter-wide\u00a0buffer<\/strong>\u00a0zone surrounding a protected stream. If all you had to work with was a paper map, a ruler, and a pencil, you might have a lengthy job on your hands. You might draw lines scaled to represent 100 meters, perpendicular to the river on both sides, at intervals that vary in frequency with the sinuosity of the stream. Then you might plot a perimeter that connects the end points of the perpendicular lines. If your task was to create hundreds of such buffer zones, you might conclude that automation is a necessity, not just a luxury.\r\n\r\n\"Illustration\r\n\r\nSurrounding a protected stream with a buffer polygon.\r\n\r\nSome tasks can be implemented equally well in either vector- or raster- oriented mapping systems. Other tasks are better suited to one representation strategy or another. The calculation of slope, for example, or ofgradient<\/strong>\u2013the direction of maximum slope along a surface\u2013is more efficiently accomplished with raster data. The slope of one raster grid cell may be calculated by comparing its elevation to the elevations of the eight cells that surround it. Raster data are also preferred for a procedure called\u00a0viewshed analysis<\/strong>\u00a0that predicts which portions of a landscape will be in view, or hidden from view, from a particular perspective.\r\n\r\nSome mapping systems provide ways to analyze attribute data as well as locational data. For example, the Excel mapping extension I mentioned above links the geographic data display capabilities of a mapping system with the data analysis capabilities of a spreadsheet. As you probably know, spreadsheets like Excel let users perform calculations on individual fields, columns, or entire files. A value changed in one field automatically changes values throughout the spreadsheet. Arithmetic, financial, statistical, and even certain database functions are supported. But as useful as spreadsheets are, they were not engineered to provide secure means of managing and analyzing large databases that consist of many related files, each of which is the responsibility of a different part of an organization. A spreadsheet is not a DBMS. And by the same token, a mapping system is not a GIS.\r\n

    1.11. Geographic Information Systems<\/h2>\r\nThe preceding discussion leads me to revise my working definition:\r\n\r\nAs I mentioned earlier, a geographer named David Cowen defined GIS as\u00a0a decision-support tool that combines the capabilities of a relational database management system with the capabilities of a mapping system (<\/strong>1988). Cowen cited an earlier study by William Carstensen (1986), who sought to establish criteria by which local governments might choose among competing GIS products. Carstensen chose site selection as an example of the kind of complex task that many organizations seek to accomplish with GIS. Given the necessary database, he advised local governments to expect that a fully functional GIS should be able to identify property parcels that are:\r\n