SUMMARY We were asked by a King County GIS Center (KCGIS) solicitation to perform a return on investment (ROI) study of the development and use of geographic information systems (GIS) within King County. Dr. Richard Zerbe and the Benefit-Cost Analysis Team at Richard Zerbe and Associates implemented a multiple-phase methodology to assess what KCGIS received for its money. A survey questionnaire was designed based on feedback from qualitative interviews conducted in the earlier phase. King County, Washington, covers an area of 2,130 square miles and had a population in 2010 of 1,931,000, making it the 13th most populous county in the United States. The subject of this study is the use of GIS within King County by county agencies. King County has approximately 14,000 employees and in 2010 its annual budget was more than $4 billion. The King County GIS Center with 28 professional GIS staff provides GIS services to all county agencies. Approximately 20 additional GIS professional staff work directly in county agencies. As many as 1,000 county employees use GIS data and applications in their daily work. Clearly, the use of GIS by the county has been hugely beneficial. An analysis of the survey responses indicates that, overall, the use of GIS--compared to not having the GIS technology--had a net benefit of approximately $180 million for the year 2010 alone. This estimate assumes that the quality and usefulness of GIS reports and other products and services remains at the same level as pre-GIS. In reality, we expect that the value of GIS-produced outputs is almost certainly higher than comparable outputs the county produced in years prior to the implementation of GIS technology. Nevertheless, on the assumption that the marginal value of output has decreased (a linear, downward-sloping demand curve), we find a lower-bound estimate of net benefits of $87 million per year in 2010.1 The benefits were broken down into benefits received from: (1) cost savings because of more efficient production of original output and (2) benefits generated from increased productivity beyond the original production level. With regards to return on investment by department in 2010, using the more conservative estimates, the Department of Natural Resources and Parks (DNRP) is estimated to benefit $87.44 million per year from having GIS technology; the Wastewater Treatment Division (WTD) and the Department of Transportation (DOT) had an estimated benefit of $54.45 million and $18.76 million, respectively (thus, all references to DNRP do not include WTD, which is reported separately). The results indicate that the Department of Assessment had an overall loss of $2.7 million--this statistic is surprising, and seems to be driven by reduction in benefits from cost savings. The reduction in benefits from cost savings is most likely driven by an increase in the number of full-time workers producing the output; that less output is produced per unit with GIS also is theoretically possible, although this is highly unlikely. It also is worth noting that the aggregate benefits estimate does not fully capture all GIS-related benefits because of missing data in the survey responses. Thus, our survey estimates are again likely to be lower-bound estimates, for we have complete cost data and incomplete benefit data (because of incomplete surveys). Conversely, we also recognize the potential that benefits attributed to GIS are conflated with other technological advancements, resulting in an overestimate of productivity gains. Given these competing forces, we expect that true net benefits are likely less than estimated here, but not a great deal so given the partial capture of benefits. In the following section, we provide a brief discussion of the response rate, specific methodological calculations used to arrive at the results, and a discussion of the results. The appendices contain statistics on benefits by department and use calculations based on different rates at which outputs are produced, pertinent questions from the survey, as well as average percentage change in productivity and cost saving by use. …
INTRODUCTION Information exchange among infrastructure companies is critical in achieving effective cooperation in the service of society. To facilitate this information exchange, the problem of interoperability between different GISs needs to be resolved and much research effort has been devoted to this issue (Fallahi et al. 2008, Pundt and Bishr 2002). The available studies, mostly concerned with constructing spatial data infrastructures, show that interoperability is facilitated by using a common data format for transmission between different GISs. The Open Geospatial Consortium (OGC) and the International Organization for Standardization (ISO) have issued data model standards that are effective in resolving data-transmission problems. In modern cities, essential supplies and services, such as natural gas, water, electricity, and telecommunication facilities, are delivered to local users via various entangled infrastructures. Achieving cooperation between these infrastructures is one of the problems concerning the administration of a city. These infrastructures typically are maintained by various companies, which make different design decisions when constructing their GISs and may use different GIS tools. This diversity results in difficulties in ensuring that the GISs are interoperable. Consider a utility company that needs to carry out a maintenance operation on one of its infrastructure elements. A service or a supply may have to be interrupted in a particular area. For example, for an electricity supply company to repair a midvoltage line, the power may need to be cut. This type of interruption can affect the network elements of other infrastructure systems such as the local exchange or a field cabinet of a telecommunication network. Moreover, in some cases, a maintenance operation requires excavation to reach a subsurface element and in this process the delivery system of another organization may be damaged. This situation is likely to arise because of a lack of information about the infrastructure laid by other organizations. To ensure that other essential services are minimally disrupted, it is necessary to devise a mechanism through which the utility companies can share their spatial data. Evidently, some infrastructure company operations, such as investment for which existing low-voltage or midvoltage line is replaced, do not require service interruption, which does affect distribution networks. In addition to maintenance operations, several emergency situations may affect the subsurface infrastructures. For example, in the event of a flash flood in a municipality, the subsurface electricity infrastructure or various distribution transformation centers may be affected. The consequence of a flood on an electricity network will be a power cut, which implies situation changes in the elements of the electricity network. These changes have a ripple effect on other infrastructures such as the telecommunications network. In the metropolitan municipalities in Turkey, maintenance planning is coordinated by the Infrastructure Coordination Headquarters (AYKOME). The duties of AYKOME include gathering the maintenance and investment programs of the infrastructure companies and issuing relevant permission to related companies taking into account the activity, when and where maintenance operations will be undertaken, with special attention being paid to those activities where excavation is necessary. AYKOME has a Web site and all the maintenance or investment plans are collected over that site. The site has mainly two user interfaces to collect the information related with the maintenance operation. In the first interface, the operator describes the job, the planning date, and the steps to complete the job. The second interface is used to submit the location of excavation. The contact person of an infrastructure company adds the location by using interconnected drop-down lists. …
INTRODUCTION The science of landscape ecology owes its existence in part to the revelations made possible by the advent of aerial photography. Only with access to aerial imagery could ecologists begin to accurately map and study the patterns of natural vegetative communities and the effects of human disturbances (Turner et al. 2001). Collection of aerial imagery for the same areas over time provided the means for studying landscape changes, both natural and artificial, but drafting land-cover maps from aerial photography was a slow and labor-intensive process (Ruelland et al. 2011). The Landsat series of remote-sensing satellites that have been collecting medium-resolution imagery since 1972 dramatically improved the ability to develop land-cover maps at the regional or continental scale (Irons and Rocchio 2012a, 2012b). Landsat imagery could be rendered into the form of a digital land-cover map using either supervised or unsupervised image-classification techniques more cost-effectively than was possible for such large areas using traditional aerial photo-interpretation techniques (Mosbech and Hansen 1994). As the Landsat digital archive grew, opportunities arose to perform land-cover change detection between months, seasons, years, or even decades for any place on the planet, making Landsat a valuable tool for conservation biology (Leimgruber et al. 2005). Numerous studies have employed Landsat imagery to evaluate anthropogenic changes in land cover between time periods spanning a year or more (Fuller 2001, Wang and Moskovits 2001, Yang and Lo 2002, Rogan et al. 2003, Boyd and Danson 2005, Ruelland et al. 2011, Pflugmacher et al. 2012). Although it is possible to directly compare changes in the raw Landsat imagery spectral reflectance values to perform change detection, the preferred approach involves classification of the imagery for each time period first, then performing change detection as a map-to-map comparison (Yang and Lo 2002). Researchers who performed their own classification of Landsat imagery for map-to-map change-detection studies could ensure that the different date epochs were classified consistently. The advent of statewide land-cover mapping programs in the United States, including those associated with the U.S. Geological Survey Gap Analysis Program (USGS 2013), provided a source of already classified Landsat imagery that could be used in change-detection analysis (Cox et al. 1994, Caicco et al. 1995, Laba et al. 2002, Pearlstine et al. 2002, Reese et al. 2002). However, inconsistencies in the Landsat imagery-classification approach used in creating successive land-cover maps make direct comparison of these data difficult (Wardlow and Egbert 2003, Wilkinson et al. 2008). The case in Florida is a good example. The Florida Fish and Wildlife Conservation Commission (FWC) developed a statewide land-cover map from Landsat data collected during the mid-1980s to the late 1980s, then created a new map using a different land-cover classification scheme with Landsat data from 2003. The period 1987 to 2003 is of interest to conservation biologists and urban planners because it represents the first 16 years during which Florida's land regulation was governed by the requirements of the landmark Local Government Comprehensive Planning and Land Development Regulation Act of 1985 (Florida Statutes [section]163.3164). Changes in land cover between 1987 and 2003 could be used to evaluate the effectiveness of local government land-use planning and regulation in curtailing urban sprawl or minimizing impacts to natural ecosystems. However, the disparate land-cover classification systems used for the two statewide inventories, as well as certain systematic errors present in the data, did not allow for direct comparison between them. This paper describes a process for normalizing the two land-cover data sets to make a valid change-detection analysis possible. METHODS Land-cover Data Two sources of statewide land-cover data for Florida were obtained from public sources. …
INTRODUCTION Background A significant body of spatially referenced, locally produced, small-scale data developed for specific local purposes exists on the hard drives and backup systems of individuals, nonprofit groups, private associations, universities, private companies, and other nongovernmental organizations across the United States. Spatially referenced data, as the term is used here, is data that refers to a particular physical location. Examples might include a university botany class project that locates and catalogs all the trees more than 15 feet tall in a small town; a homeowners' association that monitors the water quality and plant growth of the lake on which members' properties are located; a land trust that records environmental easements; or a historical museum that ties its photographic images to their physical locations, among many others. In all these cases, the data gathered by these small local originators could be of great value to others if its existence were known. At present, however, very little of this data is available from a practical perspective to other scientific researchers and potential users. It is, for all intents and purposes, completely or partially invisible. While much emphasis has shifted in recent years to providing geospatial services, there still is a strong need for service developers to be able to find and exploit existing geographic data that would make those services more effective and efficient. Many efforts at the national and state levels are being made to make government-generated spatially referenced data available to the public. In the United States and in other countries around the world, initiatives are under way to make geographic information more freely available to scientists and to the general public. In English-speaking countries, for example, UK Location (http://location.defra.gov. uk) in the United Kingdom, the Atlas of Canada (http://atlas. gc.ca/site/english/index.html), and Geoscience Australia (www. ga.gov.au) provide open access to some government-generated spatially referenced data. In the United States, initiatives such as the National Map (http://nationalmap.gov), the National Atlas (www. nationalatlas.gov), and the geospatial section of data.gov (http:// www.data.gov/geospatial/) serve similar functions. These U.S. sites contain a wider array of data than many other national portals because the U.S. federal government cannot hold copyright on materials it generates, and because some state governments make their state-level data visible through these gateways. Efforts also are under way to make international sharing of large datasets more viable, especially with regard to divergent approaches to data licensing and use rights (Onsrud et al. 2010). GEOSS Data Collection of Open Resources for Everyone (GEOSS Data-CORE 2014) is an example of an international initiative to support open access to geographic data gathered by governments across nine societal benefit areas (GEOSS 2014). Similarly, disciplinary and special purpose repositories exist to capture large sets of spatially referenced data. Examples include PANGAEA (http://www.pangaea.de) and OneGeology (http:// www.onegeology.org). Google Maps, Google Earth, Virtual Earth, and Open Street Maps provide structured environments where the user may take advantage of a data-gathering and display infrastructure to contribute data or volunteer effort to a commercial or open-data environment. In these information infrastructure environments, legal and data management issues as well as data format issues are closely controlled by the infrastructure system provider. These are not infrastructure environments for depositing or finding diverse geographic datasets, and this article does not address such environments. We conclude that no gateway exists analogous to the Global Earth Observation System of Systems (GEOSS) that could provide more visible and efficient access to millions of spatially referenced datasets drawn from disparate locally generated sources. …
