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If Only Traffic Would Match the Car, 1952, by Art Bimrose. Courtesy of The Oregonian magazine.
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5 Data and Analysis Tools INTRODUCTION Both the results of a decision-making process and the process itself are important in place-based decision making. All stakeholders should be involved in developing the questions that guide the decision making, in choosing the factors included in the planning, and in assessing the outcomes of decision making. This chapter focuses on the data and tools that are required to support sound decision making, that is, to support decisions that both are technically sound and engage the people who are impacted by them. As seen in earlier chapters, there is no single indicator, or set of indicators, that will work for all transportation and livability issues or in all places for a single issue (Sawicki and Flynn, 1996). Indicators vary with the interests of people in the community. Therefore, indicators are best selected within the context of a particular decision or set of decisions. Similarly, while tools such as Geographic Information Systems (GISs), decision-support systems, and remote sensing are aides to transportation planning, technology cannot choose the problems that are addressed. For tools to address problems, communication must take place among people, transportation experts, technology experts, and governments at all appropriate levels. This chapter explores the role that federal, state, regional, and local governments, as well as private sources, have played in making data available. It also identifies which gaps exist and what possible steps might
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be taken to increase the availability of important data to those engaged in discussions about livability in a particular place. Public data are clearly useful for decision-making purposes, but improvements in the quality and accessibility of these data are necessary. Many federal data creation and delivery programs have provided much useful information to state and local decision makers; however, these programs could be improved by making data available more frequently, for more parts of the country, and at greater resolution. Urban and suburban issues in particular require high spatial resolution and relatively high temporal resolution data, such as traffic surveillance or percentage of impervious surface coverage. State and local data are also useful but could be improved by adopting standards that allow data to be comparable across political boundaries. Much of the data needed by metropolitan planning organizations (MPOs) are geographical in nature and have these inherent scalar integration issues. If decision making is to be effective, data must be available to the public. However, placing government data in the public domain is not a simple task. For example, much more useful data could be available to decision makers at low or no additional costs if administrative data (usually of a socioeconomic nature such as percentage of children receiving lunch subsidies or families receiving aid in a district) were made accessible to others outside the collecting agencies. However, this would require care in protecting the privacy of individuals who are part of these data, additional security to prevent users from causing problems in the system, and appropriate levels of information and disclaimers so that data are not misused. Although the broadening of public access to data is essential, improvements in the quality of available data must be made as well. Quality of data refers to the appropriateness, consistency, timeliness, and level of geographic and topical detail. Individuals’ ability to access and use data may be limited by lack of access to tools, such as Internet technology, or unfamiliarity with available data, data tools, or methods of data analysis. Accessibility may also be restricted by basic social inequities such as the physical isolation of the elderly. Individuals and groups may be excluded from the decision-making process as well by agenda-setting techniques that expressly or inadvertently make the results of the decision-making process a foregone conclusion (as discussed in Chapter 4). The federal government, as well as state and local governments, have initiated many programs for collecting and sharing data and for delivering these data to the public. Some state departments of transportation are being given new responsibility for data such as environmental measuring
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and monitoring, (e.g., streamwater quality and fish passage) rather than having these data collection responsibilities remain the sole responsibility of state and federal natural resource agencies. At the same time, state departments of transportation are delegating other data responsibilities to the private sector. Still, many data collection and data and technology use issues are local in nature. In remarks made at the Woodrow Wilson Center in Washington, D.C., on March 9, 2001, Katherine Wallman, chief statistician of the Office of Management and Budget, identified the following challenges in using federal data for local decision making: 1. Obtaining reliable data: Gathering data that provide reliable information for small (local) areas is extremely expensive. Success requires adequate funding, respondent cooperation with largely voluntary federal requests for data, and education about confidentiality policies to allay public concerns about privacy. 2. Lack of appreciation for the sources of data: There is a lack of awareness of data sources. Statistical agencies have a low public profile. Private sector partnerships with federal data producers and resultant value-added products further obscure the initial federal sources of data. 3. Organization of the federal statistical system: Historically, the development of the federal statistical system in a decentralized fashion has resulted in rich but somewhat inaccessible sources of data. During the past decade, there has been considerable effort to increase the accessibility of federal data, but further interagency coordination and cooperation are essential. 4. Understanding the data: Data that are collected by various agencies, for different purposes, may be confusing to users. Although electronic dissemination has made differences in concepts, constructs, and definitions more obvious to users, informed use of data requires user understanding of data sources, reasons for collection, and data comparability. Initiatives to improve documentation are under way, but more attention is required on this front. In addition to data, people need access to analytical and decision-making tools. Raw data are rarely useful on their own. Tools are required to summarize data and to determine relationships between inputs and outcomes. The case study, presented in Box 5.1, describes such an effort in the Minneapolis-St. Paul area. The I-35W Corridor Coalition’s goals were regional community development, quality growth and diversification, and collaborative planning.
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BOX 5.1 Minnesota: North Metro I-35W Corridor Coalition I-35W is the economic engine for the part of the northeast Minneapolis-St. Paul metropolitan area. However, common development issues throughout this corridor needed to be addressed, and regional and local transportation networks were facing increases in congestion. Local demographics were changing, the housing stock was aging, and opportunities for infill development and life cycle housing were recognized. The region had to develop a plan to facilitate the shifting economic development patterns and needs. The I-35W Corridor Coalition was established and was devoted to the following vision: “To jointly and cooperatively plan for and maximize the opportunities for regional community development, quality growth, and diversification through a system of collaboration.” The coalition established a comprehensive GIS to assist in the build-out study and the development of a regional blueprint. The coalition represented the member cities of Arden Hills, Blaine, Circle Pines, Mounds View, New Brighton, Roseville, and Shoreview. The coalition partners included representatives from the county, school districts, public agencies, University of Minnesota’s Design Center for American Urban Landscape, foundations, and the business community. The coalition included a 14-member policy board of city mayors and managers. The Community Development Director Committee provided (A) The I-35 corridor. SOURCE: Design Center for American Urban Landscape, College of Architecture and Landscape Architecture, University of Minnesota.
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oversight and support for the effort. Special task forces such as those committed to GIS, housing, or transportation priorities were included as needed. Professional staff and community partners supported the effort. The planning framework was developed by the Design Center. The private sector carried out framework studies, for transportation and housing, and created and maintained databases for GIS and socioeconomic information. The success of the effort was based on building trust between communities. City mayors, managers, and directors met regularly to discuss potential projects that had multijurisdictional impacts. Proactive leveraging of both public and private investments was critical to achieve the coalition’s objectives. In addition, collaboration both within organizations and with external organizations, including the private and nonprofit sectors, was needed. The collaborative planning effort emphasized integrated subregional systems. Information sharing across political and jurisdictional boundaries was required to foster collaboration on common problems and challenges. The coalition recognized the need to have consistent, accurate, up-to-date, complete data and an efficient means of managing, recording, analyzing, and presenting the information. GIS technology was the ideal candidate because more than 80 percent of the data have a geographic component and this tool is powerful and simple to use. The coalition aimed to enable member cities to implement and access data-rich GISs and provide public access to coalition data through GISs. A coordinated and collaborative database and GIS were developed to efficiently share information to encourage consistent and cooperative subregional land use policies. The data were gathered from various pools, including agreements with counties and cable commissions, secured grants from MetroGIS and the Environmental Systems Research Institute (ESRI) secured contracts from the Office of Commercial Realtors, and reputable GIS sources. The data were purchased and installed into the coalition data server and made accessible on the coalition’s web site. A GIS coordinator was hired to gather and prepare base GIS data and the coalition’s “On-Line Atlas” was established. Cities, school districts, county departments, and state agencies submitted valuable data. This information included parcels, existing land use, future land use, generalized future land use, and zoning information. The 1997 Digital Orthophotos, U.S. Geological Survey (USGS) Digital Aerial Images, Federal Emergency Management Agency (FEMA) Flood Insurance information, and building footprints were integrated into the database. Transportation information, such as road edges, traffic assignment zones, and road centerlines, was added to the database. Environmental data included major and minor watersheds, hydrographic information, and the National Wetlands Inventory. Other infrastructure information incorporated into the GIS included county assessor’s data, sewer interceptor systems, sewer sheds, and the location of wastewater treatment plants. Socioeconomic data were also integrated into the system. Many layers of text complemented the image data. Parcel and land use data are updated quarterly using unique automated procedures. The data were compiled to create new demographic building blocks. This initiative required great effort and expense because no current Census data were available for use. A consultant was hired to merge data from schools, voter registration,
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ownership records, and municipal utility data to obtain an estimate of the size and age distribution of households. Other social and demographic data were desired, but the data attained were the best available at the time. If current Census data had been available, the coalition would have used that information instead. When the new demographic building blocks were defined, 5,000 “insight blocks” were created within the corridor. The smallest units of summary (5 to 10 households) were mapped over the entire parcel base. Multiple blocks can be put together to fit any user-defined area to give an overall picture of local needs. These insight blocks do not rely on fixed census or jurisdictional boundaries, and data in the system were more current and more flexible to use than Census data. The Design Center’s Livable Community tool for neighborhood and subregional planning was established to address critical needs within communities. These data can be updated frequently to map changing neighborhood characteristics and needs. It was expected that this tool would be applied to life cycle housing analysis, transit and traffic demand planning, economic development planning, and transit linkage between the work force and jobs. Composition and concentration data of households could be viewed as blocks. Neighborhood profiles were developed from current population and household data. These data were obtained from several state and local databases by cooperative data sharing using county tax and property data, school census data, utility billing data, and driver’s license and vehicle registration data. Note that there is no release of confidential data on individuals or households. The coalition was able to use the GIS software applications and new updated databases to identify and explore subregional patterns and trends. The patterns and trends included land use and transportation, designated town centers, neighborhood corners, the economy and environment, affordable housing, and the presence of nature in an industrial park and mixed-use area. The coalition was able to use this information to augment traffic modeling, calculate density of potential transit users, inventory natural resources, identify housing issues, attract and assist new businesses, monitor redevelopment, and identify community infrastructure. (See map of Arden Hills proposed comprehensive plan in Plate 6.) The coalition initiated a build-out project discussion. The mission of this project was to achieve regional blueprint goals through subregional collaborative actions in partnership with the Metropolitan Council. This study’s approach allowed the subregion to be viewed as a network of local economic, social, natural resource, and infrastructure systems to enhance and implement the regional blueprint. The purpose of this study was to compile a projected 20-year development pattern, clarify complex layers of system and service needs, identify combinations of appropriate “smart-growth” strategies, and provide communication links between local, subregional, and metropolitan partners. The build-out study sought to construct options from local comprehensive plans, assess land use capacity to achieve livable community principles, and conduct subregional market analysis of livable community development types. The effort aimed to coordinate multimodal transportation projects, increase mobility, and assess the implications of municipal actions for subregional and metropolitan systems, such as transportation, transit, and housing.
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The coalition has the ability to develop a preferred build-out option, which identifies implementation and financing strategies and develops subregional models of metropolitan communities. Cities can implement regional blueprints at the local level. Subregional collaboration can bridge metropolitan issues and local circumstances. The coalition will assess a transit corridor component to investigate how the regional multimodal network can be expanded into the subregion. One possibility includes acquisition and redevelopment of the local railroad line as an alternative transportation connection and link to other networks. The data, blueprint, and build-out study provided a basis for many alternative plans to be evaluated. SOURCE: http://www.I35w.org. DATA AVAILABILITY Chapter 1 discusses indicators of livability (see Table 1.2). Much of the data mentioned in that table are extant, simple, and useful to transportation planners and managers. However, a major problem with these data is that they are available for politically defined places such as states or municipalities rather than for places defined by other relevant means such as environmental or social considerations. Data related to flows would be very useful to transportation professionals and other decision makers if there was a reliable, consistent source for such information. Gaps in available data include descriptions of the flows of workers from one part of a metropolitan area to another; how long it takes to make such a trip; and what activities, such as errands, these workers might attend to along the way. Other examples of gaps include data needed for disaster preparedness: although the government provides weather data, it does not provide terrain data that are useful in determining required elevation data resolution for flood control. In terms of transportation decision making, it is difficult to collect data about past patterns of transportation investments. Yet data on the historical precedents of these investments are important as indicators of social equity, as well as sources of information about depreciation and deterioration of infrastructure and about obsolescence in terms of location, safety, and other characteristics.
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Federal Government Data The federal government makes available enormous amounts of valuable data, which are used by all levels of government, the private sector, nonprofit organizations, and individual citizens. We live in an information age; the demand for data and information, as a basis for decision making about economic, social, and environmental issues, is unprecedented. Although the federal government supplies much of these data, people are largely unaware of the sources of the data they want or use. In fact, one federal data source known as FedStat (see Appendix A for federal data sources) uses the Internet to deliver data collected and published by more than 70 federal agencies without the user’s knowing in advance which agency produced them. While it may not be necessary to know which agency produced the data, public support for government depends on public understanding of the role that government plays in people’s lives. Federal data help us understand how well (or poorly) the economy is running so that we can take steps to improve it. Data tell us about environmental quality so we can take preventive or remedial measures on critical issues and about differential levels of educational attainment and health within our populations so we can increase attention to removing barriers to equality. Much federal data are available for subnational areas such as regions and states. We know which parts of the country have higher and lower unemployment, air quality problems, and traffic congestion. In many cases, these data are collected directly by or for the federal government. In other cases, data are collected by local or state government, using federal standards, so that uniform data are available across the county. At the county level, data are much more sparse and even more difficult to find in smaller areas. The Census Bureau’s American Community Survey (ACS) promises to be a major source of small-area socioeconomic data, but it is still in the process of implementation (see Appendix A). Were this data available, the I-35W Coalition’s data collection problems would be simplified. County and subcounty data are currently available from the Census Bureau’s decennial Census. Most data collected by state and local agencies are disseminated and made useful and available by federal agencies, principally the Census Bureau, the Bureau of Transportation Statistics, the Bureau of Labor Statistics, and the Bureau of Economic Analysis. Agency by agency, database by database, federal, state, and local partnerships are essential. Agreeing upon standards is critical to this effort, since data must be uniform across all places to be meaningful in summary and for comparison among places. Especially in the field of transportation, there remain many opportunities for creating standards for data collection and reporting.
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There are multiple reasons why federal data are so valuable. They are ubiquitous, by and large available for every place in the country. Federal data are also uniform in nature, and their characteristics are well documented; therefore these data are comparable over large areas of the country. Federal data are generally of high quality, defined as appropriateness, consistency, timeliness, and relevant level of geographic and topical detail. Federal data are essentially available free of charge. Federal rules require that federally obtained data be provided to the public at no cost other than data processing fees. The United States exemplifies a commitment to the distribution of data, especially spatial data at no cost. Many believe our easy access to information has provided public and private organizations in the United States with an enormous advantage in the new economy based on information and information technology. It is reasonable to think of data as infrastructure in an information age; accordingly, a National Spatial Data Infrastructure (NSDI) was designated by executive order in 1994. A major component of NSDI required all federal agencies to develop plans for making their data available to the public (NRC, 1993, 1994, 1995). As federal agencies amplify their efforts to provide data to the public, data partnership among various levels of government have evolved. Much of this change was driven by federal agencies’ realization that insufficient resources were available at the federal level to complete any national data program at a scale that would work for place-based decision making. From the local level, the realization came that cost and work sharing with the federal government was a good way to get the data needed for local decision making. The NSDI has been enormously successful in providing a wide range of useful data. At the core of the NSDI are seven “framework” data layers: geodetic control, ortho-imagery, elevation, transportation, hydrography, governmental units, and some cadastral information. The original concept spoke of critical thematic data and included such additional data as demographics, soil type, land use, and wetlands. Seven years later we have 1:12,000-scale ortho-imagery for most of the United States, along with 1:24,000-scale digitally scanned topographic maps and a 30-meter digital elevation model (see Box 5.2 for definition of “scale”). Large portions of the National Wetland Inventory are mapped at 1:24,000, and steps have been taken to accelerate the national county soil-mapping program. In addition, the Census Bureau continues to deliver high-quality, high-resolution decennial Census data. The Census Bureau has also conceptualized the new American Community Survey, which would represent a large step in the direction of providing data for place-based decision making.
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BOX 5.2 Scale The American Heritage Dictionary defines map scale as “a proportion used in determining the dimensional relationship of a representation to that which it represents.” If 1 inch on the map equals 1 inch on the earth, the scale is 1:1. Such maps are pretty impractical, and it is typical to have a map representing much more territory in a single inch. Following the rules of the dictionary, reading the scale as a proportion, geographers and cartographers say that 1:5,000,000 is a smaller scale than 1:4,800. Proportions are read as fractions, and the larger the denominator (the number to the right of the colon), the smaller the fraction. After all, 1/32 is smaller than 1/2. In daily conversation, we often say “small scale” when we really mean to look at a small geographic area in more detail. To a geographer, this is larger scale. The casual speaker is using map extent instead of map scale. Because this is a publication of the scientific community, the term scale is used correctly to mean: small scale = less detail, covering a large geographic area; large scale = great detail, covering a small geographic area. Sometimes it is better to avoid this semantic problem by talking about coarse versus fine resolution of map detail. This is especially true in the digital age when maps can be printed at any scale. However, paper maps, often the source material for their digital counterparts, have numeric scale and use of scientific terminology is the best way to treat this information. SOURCE: USGS (2000). Sample Scales and Typical Uses Scale One Inch Covers Roughly Typical Map 1:480 0.008 mile (40 feet) Engineering design 1:1,200 0.02 mile (100 feet) Engineering plans for streets and roads 1:4,800 0.076 mile (400 feet) City map showing sidewalks and cross-walks 1:24,000 0.38 mile (2000 feet) U.S. Geological Survey topographic map 1:100,000 1.6 miles City street map 1:1,000,000 16 miles State highway map 1:5,000,000 80 miles Wall map of the continental United States
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Data from the NSDI partnerships have been useful to communities across the country, as suggested by demonstration projects conducted from July 1998 to May 2000 by the Federal Geographic Data Committee (FGDC) together with the National Partnership for Reinventing Government and five federal agencies (FGDC, 2000). These projects took place in six communities and dealt with a wide range of issues including crime prevention, land use planning and smart growth, flood mitigation, and environmental restoration. Of course, local data were needed to complement the federal data and address specific issues, and often the federal standards enhanced the ability of a given community to acquire local data from adjacent communities. Several projects in this spirit have been initiated by the federal government, such as the GeoData Alliance, which is a nonprofit organization open to all individuals and institutions using a GIS to improve the health of communities, economies, and the earth (see http://www.geoall.net). However, the GeoData Alliance efforts are still in the early stages of development. These efforts revealed other problems with the federal data. Most often mentioned was coarse granularity. Data that look detailed from a national perspective are often too coarse to address local issues such as crime, or flooding, or growth. For many community issues, higher-resolution data are needed. Besides scale, there are five other significant reasons why federal data may be inadequate for local use. Limited Availability: Sometimes federal data are not yet available for a particular location. Soil data, useful for many purposes including knowing about construction problems, are a prime example. Despite an accelerated national program, only a small portion of the 3,100 counties in the United States have adequate soil maps. Timeliness: This can be a problem for data about phenomena that are changing rapidly. Census data are an excellent example. Decennial Census data are collected only once every 10 years. As one moves further from the census year, the data become more dated and less reliable. For volatile information, Census data may be good 2 two years out of 10. Another example involves digital orthophotos and orthophoto mapping. These are techniques by which spatial data can be more accurately measured and communicated. An orthomap combines the image of an aerial photograph with metrics that allow for direct measurements of geographic location, distance, angles, et cetera. The federal program in orthophoto mapping, led by the U.S. Geological Survey (USGS) and the Natural Resources Conservation Service (NRCS, formerly the Soil Conservation Service), has been very useful for local planning
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efforts, but the images show the landscape nearly a decade ago; new images are needed, and plans to update them are uncertain. Restricted access: The federal government collects many data for administrative purpose that are not summarized or made available to decision makers, despite the 1994 NSDI executive order. Administrative data are generated from the ongoing record keeping of social welfare and other public agencies and programs. Most of these data could be acquired through the Freedom of Information Act, but the bureaucratic and financial barriers are significant; just determining how to frame a data request can be overwhelming. Examples of restricted data include information about toxic spills into rivers and local summaries of income tax, employment, and welfare cases. No data: Some data are critical, but the federal government has no funding for developing data that could be useful for local decision makers. A prime example is that of land use data, which are of significant interest to many agencies but the primary responsibility of none. Therefore, we have never had a detailed national land use map. We get some satellite data, but no systematic classification into land use categories. The USGS has made several short-lived attempts to develop land use data or standard classifications that could be used across the country. Uncoordinated data: Interoperability among datasets is limited by the use of different geographies, nonstandard codes, unique computer systems, and narrow visions. Too often, data are collected for a single purpose and are not suitable for comparison with other data. The U.S. Department of Transportation (DOT) is organized according to transportation mode (e.g., Federal Highways Administration, Federal Railroads Administration). In the past, communication among these mode-specific administrations has been limited and highly structured. Although there are some ongoing efforts to facilitate cooperation and coordination among these administrations (e.g., the “OneDOT” program), cooperation with respect to data collection and sharing has been particularly difficult. Data collection, database maintenance, and data quality assurance or quality control are difficult and expensive. The mode-specific administrations have understandably focused past efforts on collecting data for particular purposes. However, integrated digital geographic databases impose new requirements that necessitate new data collection, maintenance and quality assurance-quality control efforts by these administrations. Unless the benefits can be demonstrated to mode-specific administrations, it is difficult to see how they will change their data collection and process
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ing activities. Unfortunately, this is a “chicken and egg” problem since it is difficult to demonstrate benefits without good data. Other Government Data The prime responsibilities of most state and local governments do not include data production. Instead they have primary administrative responsibilities that require data collection as part of their day-to-day activities. For example, as part of the property tax system, local governments collect data on housing value that can also be used to monitor inner-city decay or revitalization. Building permits, used to protect the health and safety of inhabitants, can also be used to monitor the spread of the city into the countryside. In a few cases, state and local governments do gather and publish data for use by others. A number of states have produced detailed land use maps, allowing data to be distributed widely. In the Twin Cities of Minneapolis and St. Paul, regional government cooperated with state government to license current, accurate, street centerline data (including address ranges on all street segments) from a private firm, including access to all state and local government offices, which are accessible at no charge. There are multiple reasons why state and local government data may be less than ideal. Some of these reasons are discussed below. Cost: Cities and states are not restricted from charging for their data, and high costs can limit access to these resources. High-cost data make for uneven access, which becomes an equity issue. However, a reasonable rationale for the sale of data is to cover the cost of creating the database. Cities and states are not mandated to collect and distribute data. They have functional roles, and data distribution is an extra service. It is reasonable for them to charge a fee for those who need those data, but that fee can discourage access for some legitimate users of the data. Refusal: Since not all states require that government-collected data be made available to the public, refusal to share data is common. In some cases, privacy restrictions prevent the release of data, but interpretation of privacy rules varies greatly. Another problem is the wide variation in interpretation of federal privacy rules. Federal data about the number of employees at a location are important for transportation planning. (ES202 data are available at http://www.bls.gov/cewover.htm.) Although the State of Wisconsin provides easy access to these data for research purposes, they are not available in many other states. Wisconsin helps protect the privacy
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of employers by prohibiting the publication of information about those firms included in the data and requires that researchers be discreet. Inability: Much data are stored in older information systems designed for a particular purpose, and these systems are often incapable of providing the data in any other way. For example, a city assessor’s system created to produce property tax records might be unable to answer questions about the number of three or more bedroom apartments in a neighborhood. Such basic information is used for estimating population capacity and therefore transportation demand. All the relevant data are in a computer system, but the system was created before commercial database packages were available, and any unique report would require the services of a programmer in a long-forgotten computer language. Quality: Data may be incomplete, badly documented, or inappropriate for the intended use. An example is the ES202 employment data (see above) collected by states. Information on employment is collected as part of unemployment insurance programs—information that could be useful for transportation planning. Because those collecting the data are focused on the insurance issue, they do not require reporting firms to adhere to the rule about separating employees by place of work. All 8,000 employees of the Minneapolis public schools, working at more than 100 sites around the city, are reported as working at the school district’s downtown headquarters. The data are more than adequate for administration of the unemployment program, but lacking in usefulness for indicating jobs in particular parts of the city. Lack of standards: Data from various counties may be of the highest standards, yet collected in nonstandard ways, so it becomes difficult or impossible to compare data across counties. A prime example is travel behavior inventories. These are taken infrequently, and standards are not uniform. Federal standards could help solve this problem. No data: The basic parcel map, showing where people live, does not exist in digital form for much of the country. The Western Governors’ Association (2001) is working to resolve this problem west of the Mississippi, in cooperation with the Bureau of Land Management. Guidelines, good geodetic control, and some kind of financial support seem to be the necessary ingredients. Federal paradox: State and county governments are unwilling to give their data to any activity involving the federal government because the federal government is then required to make the data available to everyone at no cost. State and local governments often
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need the funds that come from selling their data to support ongoing system maintenance; if they give their data to the federal government, the market for their information is lost when buyers can get the same information free of charge from the U.S. government. Private Data Sometimes private data are the best available. For example, Grubb-Ellis has data available on commercial office space for major markets across the country. Its data include information of total square feet, vacant space, and rents. No government organization has such information. Dun & Bradstreet sells information about firms, their location, and employment. Similarly, Dodge-Polk is the best source of national data about registered motor vehicles. All of this information could be useful in transportation planning. Of course, private information is usually available at a price and users will have to decide whether they can afford it or whether it is sufficiently valuable for their purpose. Private firms also have significant amounts of data on households and small geographic areas—data that can be useful for direct marketing and other forms of advertising. Included in these private data are current population estimates, estimates of income, and data about expenditure patterns. The population estimates build off previous Census counts and building permit data collected from local government offices. The expenditure data are based on data collected at the point of sale by asking a customer where he or she lives or from analysis of credit card purchases. Private firms also have qualitative data that they use to determine the needs and desires of communities. Firms such as Claritas have worked hard to develop psychographic profiles of small geographic areas that, together with quantitative data, help organizations determine where to focus their promotional efforts. Some types of people are more likely to favor all-terrain vehicles, and Claritas can help identify communities in which target populations are concentrated (Weiss, 1988, c. 2000). Such data and information have multiple applications for place-based decision making and community planning. Much of the data held by the private sector already exist in the public sector, but the private sector data are more useful. Data on availability of office space are collected by local government assessors, but Grubb-Ellis’s data are more current and comprehensive. Individual states have motor vehicle registration data, but Dodge-Polk makes such data comparable across the country. State governments collect data on employment, but Dunn & Bradstreet data is available for individual firms and location. The Census provides demographic data every 10 years, but Claritas updates
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its information regularly and adds both quantitative and qualitative data to this base. Data Collected by Communities and Smaller-Level Governments Frequently communities must collect their own data because they cannot locate or afford data from others. Most often the factors important to a community concerning a particular issue have not been considered at all by private or government organizations, and data about them do not exist. Citizen attitudes about an issue are one example of such data. The community is faced with two obstacles as it considers collecting data about these issues. The first is cost. If the issue is important only to that local community, no one else will be willing to share the expense, which could be considerable. The second issue is quality. Data will have to be of sufficient quality to be credible to other participants in the discussion. If the quality is too low, it will be dismissed. There is the chance that even high-quality data will be dismissed because the issue is deemed irrelevant, so the community might be wasting its money no matter how well it has done its work. Access to Data and Analytical Tools Data have no value unless they can be accessed and used. Tools are needed to aid communities in accessing and analyzing data, especially those with limited technical and financial resources. Larger cities and towns are likely to have the resources to be self-sufficient, and smaller cities and larger community-based organizations have taken advantage of falling prices for hardware and software to become self-sufficient as well. However, neighborhood and other community groups typically depend on pooled efforts and the goodwill of others (Leitner et al., 2000; Sawicki and Peterman, forthcoming). Breakthroughs in providing access to data and tools are coming rapidly, but most of this access has been at basic levels that do not approach the sophisticated levels of analysis available to professional planners. Increasingly, data access is provided over the Internet. For example, Census data are available over the Internet (see http://www.census.gov), and plans call for making data from Census 2000 available over the web, via American Factfinder (see Appendix A). Increasingly, federal, state, and local governments are finding that providing their data free on the Internet saves them the cost of servicing customized requests, while allowing more people access to their data. A growing number of sites are providing community data and maps via the web. The Geography Network attempts to be a clearinghouse for a
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wide variety of users and providers of data (for example, the Network provides on-line delivery of demographic data from CACI International Inc., for a geographic area surrounding a user-provided address [see http://geographynetwork.com]). National Geographic provides a range of useful maps from its web site (see http://www.nationalgeographic.com). Universities and others are providing on-line access to Census maps and analysis (e.g., the Ralph and Goldy Lewis Center for Regional Policy Studies at the University of California-Los Angeles, which focuses on residential segregation in the Los Angeles area; http://www.sppsr.ucla.edu/lewis/hs~CensusUpdates.html). Many counties provide on-line access to parcel-level maps and data about housing values, recent sales, et cetera (for an example, see http://www.co.dakota.mn.us/assessor/real_estate_inquiry.htm). Related to Internet access is data provision in kiosks and on mass-produced disks. In all cases, the data provider incurs a substantial cost in preparing the data and documentation for distribution, but then saves money in not having to spend time with each data requestor and customizing a response. Users are given quicker, more consistent, and cheaper access to data. Data over the Internet (and related technologies) are sometimes attractively packaged with graphs and maps that help users see patterns in the data. Often, however, communities must manipulate and combine data to make sense of them in terms of their own livability goals. The Orton Family Foundation is developing a new land use simulation and visualization program called CommunityViz. This is a rare example of software designed to combine various aspects of community planning and to make the results available to the local community. More information can be obtained at the foundation’s web site (http://www.orton.org/). One of the more difficult problems facing policy analysts, stakeholders, and decision makers is the choice among competing forecasting methods and models. As a case in point, consider land use-transportation models that forecast future travel demands and land uses. An urban or regional system is a web of trends and interactions that evolve at different speeds, ranging from instantly changing subsystems such as travel patterns; to medium-speed subsystems such as workplace and housing locations that take multiple years to change; to long-range subsystems, such as transportation, communication, and utility networks; and land-use, which can require decades to change. Consequently, land use-transportation models are complex and often require simplifying assumptions for tractability (Wegener, 1994). In addition to data and computer requirements, the choices among land use-transportation models actually includes selecting which “story” you believe about how cities and regions evolve. Policy makers and decision
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makers often do not have the background or time to evaluate the assumptions, strengths, and weaknesses of these theories of change. The result is that major infrastructure and policy decisions are often based on forecasts from methods whose validity is unknown. The need to supplement data with description information is well recognized. Metadata, or “data about data,” allow the user to assess the appropriateness of the data for the task at hand. There are standard templates required by federal agencies and transnational organizations for data. An analogous concept is a metamodel, or a “model about the model” (i.e., a high-level [semantic rather than formal] description of the model). This will require developing standardized and understandable metamodel templates for particular modeling domains (e.g., travel demand, demographics, hazards). This information could be delivered within a software environment using the common agent-based technology of wizards that guide users through complex software installations or operations. SUMMARY AND CONCLUSIONS The federal government plays a significant role in providing data to support decision making at the national and subnational levels. Its various statistical arms collect and disseminate data that are critical for decision making by all sectors and at all levels. Other critical data are collected by state and local governments and reported in a standard form that adds to the data resource base of the country. Yet there remain gaps in the data, which makes it difficult to make sound place-based decisions. The major gaps include the following: Certain data are not available on a sufficiently timely basis (for example, decennial Census data for small areas). Such demographic data collected only once a decade may have been adequate in an earlier period, but this is no longer true. Proposed changes to the decennial Census, such as the American Community Survey, would provide for the collection and dissemination of smaller-area data on a much more timely basis. Often data is not available at a scale that are adequate for local decision making. Fine-resolution data are collected by state and local government and by private enterprises. However, privately collected data are frequently prohibitively expensive for community use. Also, data collected by state and local governments at finer resolutions could be used more efficiently if national standards were in place. The federal government is in the best position to lead such a standards effort.
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Data coverage is patchy and inconsistent. For example, only a fraction of the counties in the United States have digital parcel data, and few of those who do have it follow a common standard. Remedying this situation requires additional resources. The Bureau of Land Management (BLM) and the Western Governors’ Association are working to remedy this situation, but they struggle with limited resources. BLM has begun to extend this cooperative effort to the eastern states, but it will require even more resources. Land use information is critical for transportation and other planning, yet there is no federal program to provide this information or to define standards for its collection by state or local government. The creation of standards would be the least expensive approach for the federal government to address. Federal support for collection of land use data in communities with limited resources would be needed Some federal data could be quite useful for local decision making, but additional effort is required to clarify collection and distribution procedures. The ES202 data are a prime example. Data are often collected without regard to actual work location of employees. The individual state agencies that collect data under federal guidelines have varying understandings of whether these data can be distributed to anyone outside their individual agencies for any purpose. Federal data programs have to be reviewed and revised because they are incompatible with other federal data collection activities. In particular, the various mode-specific administrations of the U.S. Department of Transportation collect data that are difficult to combine into a general picture of transportation services or needs. The rules making all data “owned” by the federal government free to all potential users limit the willingness of various public and private entities to share data with the federal government. This is counterproductive to good public decision making. Approaches should be pursued to limit these rules where appropriate. The federal government is taking advantage of developing technologies for distributing data via the Internet, thereby making them accessible to communities across the country. What is lacking is access to robust models that allow communities to see the implications of alternative transportation alternatives. In part, this is due to the lack of public access to easy-to-use models. A more basic problem is the consensus about which models work best in a particular situation. This underscores the need for greater communication among public, private, and professional sectors. Just as private citizens and other decision makers need models to see and
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understand alternatives, traffic professionals need to interact with academic and technical communities so that they can anticipate tools that may be available in 5, 10, even or 20 years and so that development of the tools can keep pace with emerging problems. This will help communities protect their cultural, environmental, and social resources and plan to meet their own needs and those of future generations. REFERENCES FGDC (Federal Geographic Data Committee). 2000. NSDI Community Demonstration Projects Final Report. Available at http://www.fgdc.gov/nsdi/docs/cdp/. Accessed October 1, 2001. Leitner, Helga, Sarah Elwood, Eric Sheppard, Susanna McMaster, and Robert McMaster. 2000. Modes of GIS provision and their appropriateness for neighborhood organizations: Examples from Minneapolis and St. Paul, Minnesota. URISA Journal 12(4 Fall):43-56. NRC (National Research Council). 1993. Toward a Coordinated Spatial Data Infrastructure for the Nation. Washington, D.C.: National Academy Press. NRC. 1994. Promoting the National Spatial Data Infrastructure Through Partnerships. Washington, D.C.: National Academy Press. NRC. 1995. A Data Foundation for the National Spatial Data Infrastructure. Washington, D.C.: National Academy Press. Sawicki, David S., and Patrice Flynn. 1996. Neighborhood indicators: A review of the literature and an assessment of conceptual and methodological issues. Journal of the American Planning Association 62(2):165-183. Sawicki, David S. and David R. Peterman. 2002. Surveying the Extent of PPGIS Practice in the United States. In W. J. Craig, T. M. Harris, and D. Weiner, eds., Community Participation and Geographic Information Systems. London: Taylor and Francis. U.S. Geological Survey. 2000. Map Scales Fact Sheet 038-00. Available at http://mac.usgs.gov/mac/isb/pubs/factsheets/fs03800.html. Accessed on March 6, 2002. Wegener, M. 1994. Operational urban models: State of the art. Journal of the American Planning Association 60(Winter):17-29. Weiss, Michael J. 1988. The Clustering of America. New York: Harper and Row. Weiss, Michael J. c. 2000. The Clustered World: How We Live, What We Buy, and What It All Means About Who We Are. Boston, Mass.: Little, Brown. Western Governors’ Association. 2001. Western Cadastral Data and Policy Forum Report. Available at http://www.westgov.org/wga/publicat/cadastral.pdf. Accessed October 1, 2001.
Representative terms from entire chapter: