||| Urban Dynamics | Temporal Mapping | Objectives | Benefits | Regional Assesments | Papers/Abstracts | Data/Images |||
William Acevedo, U.S. Geological Survey
Ames Research Center MS 242-4, Moffett Field , CA 94035
Timothy W. Foresman, University of Maryland Baltimore County
5401 Wilkens Avenue, Baltimore, MD 21228-5398
Janis T. Buchanan, Johnson Controls World Services
Ames Research Center MS 242-4, Moffett Field , CA 94035
The U.S. Geological Survey (USGS), in partnership with the University of Maryland Baltimore County, is using historical maps and satellite images to map human-induced land transformations for the Baltimore-Washington metropolitan area. This work builds on an earlier effort that documented the historical urban development for the San Francisco Bay area. That effort used a geographic information system to compile a database that provided a visual and historical perspective of the urban growth experienced in the Bay area between 1850 and 1990. Historical overviews of urban development can be used to provide insights into the future. The Bay area work was inspired by the desire to draw upon the USGS's rich 100-year topographic map, and 20-year Landsat satellite data archives. A methodology was developed to combine the information from a variety of sources into an integrated, multi-scale, and multi-resolution dataset. Temporal urban mapping is used to examine past landscapes by incorporating historic maps, census statistics, and commerce records to generate a progressive geo-referenced representation of the past changes in the region. Contemporary analysis focuses on the use of remotely sensed data, existing digital land use data, digital census information, and a variety of Earth science infrastructure data, such as Digital Line Graphs, Digital Elevation Models, and key ancillary demographic information. The resulting database of temporal urban demographic changes provides an ideal source of test data and information for both urban geographers and global change research scientists.
Most major metropolitan areas face the growing problems of urban sprawl, loss of natural vegetation, loss of open spaces, and a general decline in the spatial extent and connectivity of wetlands and wildlife habitat. The public identifies with these problems as they see residential and commercial development replacing undeveloped land around them. These problems can be generally attributed to increasing population. As a result, urban growth rates show no signs of slowing especially when viewed at the global scale. Cities have gone from being small isolated population centers to being large connected economic, physical, and environmental features of our planet. The problems that society faces because of the growth of our cities and a concentration of human populations is just beginning to be fully recognized as a significant global problem. In his MacArthur Award lecture, Peter M. Vitousek (1994) states that "Three of the well-documented global changes are increasing concentrations of carbon dioxide in the atmosphere; alterations in the biochemistry of the global nitrogen cycle; and ongoing land use/land cover change".
These land use/land cover changes can be immense, but difficult to grasp when
they occur incrementally. Recently, the data from Earth resource monitoring
satellites has dramatically illustrated the rates at which these human-induced
changes are occurring. Although, we see our cities grow month by month and
year by year, we escape realization of just how much they have grown over a
longer period. In particular, modern urban settlements are characterized by
the proliferation of asphalt and concrete along with the displacement of
agriculture and forestland. We fail to see how this growth, region by region (Figure 1), amounts to a significant global process.
Urbanization exerts heavy pressures on surrounding lands. The impact urban land has on economic and environmental systems is extremely large, compared to its spatial extent. It is imperative that we understand the determinants of land use (demographics, technology, levels of affluence, etc.) so that future patterns of land use and land cover can be projected, planned, and managed under sustainable conditions.
Temporal Urban Mapping
The concept of building a temporal database of urban land transformation parameters to study the impacts of urban development is being explored by a collaborative team from the U.S. Geological Survey and the University of Maryland Baltimore County. A methodology has been developed and documented that combines the information from a variety of sources into an integrated, multi-scale, and multi-resolution database. The multi-theme temporal database includes urban development, principal transportation, hydrography, and demographics. Temporal urban mapping reconstructs past landscapes by incorporating historic maps, census statistics, and commerce records to generate a progressive geo-referenced picture of urban change within a region. Contemporary urban mapping focuses on the use of remotely sensed data, existing digital land use data, digital census information, and a variety of Earth science infrastructure data, such as Digital Line Graphs, Digital Elevation Models, and key ancillary demographic information.
The database is being developed to illustrate the spatial patterns and interactions among the physiographic and socio-economic variables contributing to urban growth. Historical overviews of urban development provide insights into the future. This database will be of much use to urban and regional planners, policy and decision makers, Earth scientists, and global change researchers for measuring trends in urban sprawl, monitoring impermeable surfaces, analyzing patterns of water pollution and sedimentation, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast future areas of urban growth. The economic, environmental and political consequences of informed growth decisions is vital to the millions of people living in large metropolitan areas.
This paper reviews the underlying philosophical foundations that originated and now support the credibility of developing a temporal database for the analysis of the spatial patterns and rates of change resulting from human impacts on the land surface.
The USGS first initiated urban mapping research activities as a project contributing to the U.S. Global Change Research Program. The USGS's Human-Induced Land Transformations (HILT) project was undertaken to understand the urban transition from a historical and multi-scale perspective sufficient to model and predict regional patterns of urbanization 100 years into the future (Kirtland et al., 1994). The original project consisted of three components. The first component involved a significant effort in mapping the growth of urban development for the San Francisco Bay area (Bell et al., 1995). This research involved using the historical archive of USGS topographic maps and Landsat satellite images to delineate urban extent. Visualization of the data led to the production of a computer animation illustrating the map sequence. A videotape of the animation was first presented at the Association of American Geographers annual convention in San Francisco (Acevedo and Bell, 1994).
The second component involved developing a cellular automaton urban growth model for the San Francisco Bay Region (Clarke et al., 1996). The model was derived by adapting the Clarke wildfire behavior model and its calibration techniques to the land cover context (Clarke et al., 1995). Calibration of the model relied on the temporal urban extent database.
The third component of the HILT project examined multi-scale extension and interrelationships of various urban parameters and also explored applications of the temporal database.
TEMPORAL MAPPING IN THE SAN FRANCISCO BAY REGION
The Dynamic Urban Mapping component of the HILT project assembled a small temporal urban land characteristics database for the San Francisco-San Jose-Sacramento metropolitan area. The database provided a visual and historical perspective of the urban growth experienced in the area between 1850 and 1990. Past landscapes were reconstructed using historic maps, remotely sensed data, land use/land cover maps, and Digital Line Graphs (DLG's) to generate a geo-referenced map base documenting the changes in the region. Database development was limited to the temporal mapping of urban and built-up areas, and principal transportation routes. Data visualization research activities applied single frame computer animation techniques to visualize the database and to aid in understanding the spatial and temporal transformations that occurred over time. The study area corresponded to two Landsat Thematic Mapper scenes covering the San Francisco Bay region and surrounding lands extending into the Central Valley and Sierra foothills. The 1990 census recorded a total population of over 8 million inhabitants within the study area.
Urban development was generally defined as built-up areas characterized by buildings and a systematic street pattern. Principal transportation was defined as the primary roads, railroads, and other transportation features that provide the foundation or seeding infrastructure for urban development. A modification of the land use and land cover classification system described by Anderson and others (1976) was adopted for the study. The level II classification definitions were modified slightly to accommodate limitations in the source materials and to better categorize land use intensity.
The boundaries for urban development and principal transportation were derived from a variety of sources ranging in scale and resolution. Sources included historical topographic maps (Figure 2), land use and land cover maps, DLG's for
Figure 2. A portion of the 1899, 1943, and 1961 USGS 1:62,500-scale topographic maps for San Jose, California. The maps show key elements such as the systematic street pattern, building locations, and map tints that were used to interpret the extent of urban and built-up areas.
transportation, aerial photography, and Landsat Multispectral Scanner and Thematic Mapper data. The compilation of urban development was accomplished by a combination of manual photo-interpretation, and automated spectral classification. The type of source materials available determined the actual procedures that were used. A geographic information system (GIS) was used to assemble and integrate the data. In the San Francisco study, the available source materials permitted the compilation of eight maps of urban extent and transportation for the years 1800, 1850, 1900, 1940, 1954, 1962, 1974, and 1990 (Figure 3).
Figure 3. A historical perspective of urban development for the San Francisco Bay region. The image maps of urban extent dramatically convey the spatial patterns and rates of urban land use change experienced between 1800 and 1990.
Urban extent was compiled from topographic maps by interpretation and delineation on mylar map overlays. Historical map sources were primarily 1:10,000-scale Coast Survey maps, 1:62,500-scale and 1:24,000-scale USGS topographic maps, and 1:50,000-scale Army Map Service maps, compiled in the 1850's, 1900's, 1940's, and 1960's. Built-up boundaries were determined by the existence of a dense systematic street pattern, the relative concentration of buildings, and in some cases the extent of the urban tint found on some maps. After map interpretation, the mylar overlays were digitized and input into the GIS.
Land use and land cover maps provided a significant resource for selected areas. Land use information compiled for nine bay area counties by the Association of Bay Area Governments (ABAG) contributed to the 1954 and 1962 urban delineations. USGS digital land use and land cover (LULC) maps and associated overlays at 1:250,000-scale (Loelkes, 1977; Fegeas et al.,1983) processed by the Geographic Information Retrieval and Analysis System (GIRAS), contributed to the 1972 urban delineation.
Landsat spectral data provided the most recent, spatially continuous, and consistent coverage. The 1974 and 1990 data layers were determined by automated image processing procedures which combined the Landsat spectral classification with the road network density derived from DLG's, and the GIRAS land use and land cover data. The Landsat boundaries are characterized by the spectral signatures commonly interpreted as concrete, asphalt, buildings, roads, residential neighborhoods, and commercial buildings.
Visual and statistical analysis of the data (Figure 4) reveals a slow steady
Figure 4. A plot of area summaries for urban development from the San Francisco temporal database.
urban growth that starts in 1850 during the California gold rush and continues up to the turn of the century. The growth rate increases slightly up to World War II. In the years after the war, the growth rate shows a dramatic increase that continues through the 1950's, 1960's, and into the 1970's. Although still increasing, the growth rate appeared to have slowed slightly during the 1980's, compared to the growth boom experienced after World War II.
The study also produced a unique, time-series animation of the database showing the explosive urban growth experienced over the last 190-years. The animation was created by first generating intermediate datasets for each year from an interpolation of the known data. The interpolated data was then composited with a shaded relief background image, an image of present day hydrography, and an image indicating the year represented by the data. The resulting 190 yearly images were transformed into an animated movie file that could be displayed or recorded to video tape.
CONCEPTUAL FRAMEWORK FOR TEMPORAL MAPPING
The original impetus for building a temporal database of urban extent can be traced to five simple ideas. The first assumption was the recognition that urbanization was an important component of global change research which was not being addressed by any of the national research agendas. This initiated the desire to apply USGS's experience in land surface characterization to the mapping of urban areas. The second assumption was that historical data on urban extent could be used to calibrate a cellular model in order to predict regional patterns of urbanization. Having identified a specific application, database development needed source materials to be identified. The third assumption was that USGS topographic maps could be used to derive the extent of urban and built-up areas for the last 100 years. Followed by the assumption that Landsat data could be used to map development from 1972 to the present. The last assumption that started the San Francisco temporal mapping work was the notion that a time-series animation could be used to better convey the dramatic land use changes resulting from urbanization.
The reality of actually accomplishing the tasks outlined in the initial proposal required the transformation of vague ideas and assumptions into clear and technically feasible constructs that could be used to guide the implementation and database development effort. The San Francisco mapping work served as a testbed for defining some of the original ideas. The Baltimore-Washington study then provided the focus and the multi-disciplinary team needed to fully develop the philosophical principles that establish the foundation upon which one can support a program for building a temporal GIS database of urban development. These constructs would be required to extend the research approach from a regional base to a national base for retrospective urbanization analysis.
Applications for a Temporal Database
Although the original San Francisco research proposal called for database development to contribute to model calibration and global change research, it was quickly concluded that the data supported a much broader range of applications. The applications and data requirements of urban geographers, urban planners, cartographers, hydrologists, educators, policy and decision makers and global change research scientists have been documented by colleagues, specific contacts, literature review and regional workshops. The database has immediate applications in monitoring urban sprawl, watershed analysis, environmental assessment, analyzing patterns of sedimentation, hydrologic modeling, sustainable development studies, land surface degradation, and in modeling surface temperature related to urban development. These applications should provide the data necessary to make informed growth decisions, which are vital to the millions of people living in large urban centers.
Core Data Needs
The original assumption that historical data could be used to calibrate a cellular automaton urban growth model focused the database development to the data requirements of the model. The original data specification was thus limited to defining the extent of built-up areas. Eventually, the database effort was increased to include topography, transportation routes, and protected areas. Temporal database requirements are now identified by a much broader suite of applications and the data needs of a more varied group of collaborators and data users. A wish list of twelve core themes (data layers) was compiled for the Baltimore-Washington study. This list is comparable to the high-priority core data sets identified by the International Symposium on Core Data Needs for Environmental Assessment and Sustainable Development Strategies (Estes et al., 1994). Some of the core data sets identified for urban mapping include built-up areas, principal transportation, hydrography, shorelines, wetlands, population density, agriculture, forestland, census and legal/statistical boundaries, economics, and land cover. Ancillary single date themes include topography, watersheds, and soils.
Early assumptions neglected to define the technical framework under which contributing organizations and technologies would interact to produce a geospatial database of urban land transformations. In many ways, the National Spatial Data Infrastructure (NSDI) concept of a framework dataset has defined the characteristics of a temporal urban database. The development of standardized definitions, adoption of classification schemes, data specifications, documentation of procedures, compilation criteria, guidelines, description of source materials, and metadata compliance have all been addressed by this study. Policies, standards, and procedures were documented by the project for subsequent application to other regions. The various urban data themes are characterized by a multi-source, multi-scale dataset comprised of the "best" available data whose accuracy is dependent on the available source materials.
Map Data Sources
The assumption that USGS topographic maps could be used to derive the extent of built-up areas was expanded to include other sources of historical maps for more complete temporal coverage. Many reliable sources of cartographic data have been found that were created prior to the establishment of the USGS mapping program. Complementing the USGS archives are maps by early cartographers and map publishers, the U.S. Coast Survey, Army Map Service, Defense Mapping Agency, and many companies making commercial road maps.
Map scale is an important consideration when searching for source materials. Although the multi-source nature of the database permits varying scales, a set of documented guidelines or suggestions can assist with map library field searches and offer consistency in data acquisition. In the authors experience, data source surveys of major areas of development have generally encountered adequate coverage at scales of approximately 1:62,500 and larger. Obtaining full regional coverage has, in some cases, required using maps at 1:125,000-scale and on rare occasion 1:250,000-scale. Our current experiences suggest a guideline for using scales no smaller than 1:100,000.
Digital Data Sources
The assumption that Landsat data alone could be used to map urban development has been expanded to include many other digital and remotely sensed data sources. Data such as DLG's, GIRAS land use and land cover data, SPOT satellite data, and North American Land Characterization Pathfinder (NALC) triplicates can be employed for regional mapping. Digital orthophotos, digital raster graphics, and aerial photography are other appropriate sources available for more localized mapping. Several proposed government and commercial space borne sensors promise to offer other data sources that can be utilized in the urbanization mapping methodology.
Spatial resolution becomes a factor when working with remotely sensed data. The 60-meter spatial resolution of the MSS sensor is marginal for analysis of an urbanized area. The 30-meter spatial resolution of the TM sensor is much better at discerning the detailed street and building patterns that are used to characterize an urban area. A spatial resolution of 15-meters is, most likely, the optimal resolution for the stated application given the processing and data storage problems that higher resolution data would entail.
The vague notion concerning use of a time-series animation to help convey land use change just barely touched upon the critical role that data visualization must play in quantitative spatial data analysis. Animated cartography, video production, multi-media presentation, and traditional visualization tools are essential to effectively visualize a temporal database and to assist in conducting advanced spatial analysis of complex interacting components. Single frame animation tools complement those available for creating standard planimetric views. Although much visualization research is needed, the use of contours, shaded relief images, image blending, 3-dimensional perspective views, and time-series animation are available for the display of multiple datasets.
Development of a temporal database can be expensive both in labor and in the technology resources required. This project is aware of the need to emphasize procedures that streamline and automate the compilation process. New procedures and techniques are being investigated in order minimize the level of effort required to build the database. The use of remotely sensed data, in combination with other digital resources has already provided a savings over labor intensive, manual delineations. The conversion of analog maps to digital raster scans was examined as a source for online screen digitizing. Research in image processing techniques to automatically extract road networks from the digital map may prove useful in the future for both urban and transportation delineation. The use of smaller scale source materials, or larger minimum mapping units is under evaluation for the tradeoffs in cost savings versus the loss in information content.
The Baltimore-Washington Regional Study
Building on the San Francisco mapping activities a collaboration was initiated between USGS, UMBC, and U.S. Census Bureau to extend temporal mapping into the Chesapeake Bay region. The Baltimore-Washington Spatial Dynamics and Human Impacts Study is assembling an integrated and flexible temporal urban land characteristics database for the Baltimore-Washington metropolitan area (Figure 5).
Figure 5. Image maps of urban development for Baltimore, Maryland. Data shows the growth experienced between 1792 and 1992.
The database provides a historical perspective of the urban growth experienced in the area between 1792 and 1992. The Baltimore-Washington area was selected as a study site due to the dramatic increase in urban development experienced over the last 200 years. Currently, with more than 7 million inhabitants, the Chesapeake Bay region is one of the nation's fastest growing metropolitan areas. A multi-year phased approach was adopted for the study.
Phase I focuses on the Baltimore metropolitan area and contributes to the research needed to broaden the scope of the larger regional mapping effort. Phase I tests technique development based on the previous San Francisco work and interagency data integration for a 25-minute square centered on the city of Baltimore. The phase II regional analysis proposes mapping a 2-degree square centered on Washington D.C. while Phase III and IV will focus on experimental mapping of selected themes and the analysis of spatial patterns and rates.
Phase I and II database development efforts include temporal mapping of urban development, principal transportation, hydrography, and population density. Mapping from 1792 to 1966 reconstructed past landscapes through the use of historic maps, census statistics, and commerce records to generate a progressive geo-referenced picture of the region. Contemporary temporal mapping from 1972 to 1992 focuses on the use of topographic maps, commercial road maps, Landsat remotely sensed data, existing digital land use data, and Digital Line Graphs for transportation and hydrography.
Other activities include, data visualization research, NSDI metadata compliance, user outreach, and science modeling applications. A multi-disciplinary team has expanded on methodology, definitions, and collection criteria used to define the various data layers, ensuring consistency in data definitions and data collection techniques among the different collaborators. Extensive documentation on source materials and process steps were recorded for use in defining the associated metadata files. Animated cartography and other visualization techniques were examined for their use in visualizing the database (Masuoka et al., 1996). Interpolation methods for the animation were improved to better simulate the leapfrog development and infilling that characterizes urban growth phenomena.
Similar to the San Francisco study, urban development was defined as areas of intensive use, with much of the land covered by structures. The limits of the built-up area were characterized by the existence of a systematic street pattern, and the relative concentration of buildings and associated intensive use areas (Crawford et al., 1996). The generalized urban boundary includes any undeveloped land that is completely surrounded by developed areas. The modified Anderson classification scheme was adopted and compilation criteria and guidelines were established prior to data delineation. Principal transportation was defined as the primary roads, railroads, seaports, airports, and other transportation features that provide the infrastructure for urban development. Compilation criteria, such as connectivity, mobility, lineage and alignment were developed to accommodate limitations in the source materials (Clark et al, 1996). The hydrography data layer was defined as the reservoirs and shoreline changes resulting from urbanization. The Anderson classification scheme for the water categories was considered satisfactory for this data layer.
Database development relied on a variety of source materials ranging in scale and resolution. Historical maps from the Library of Congress, and the Maryland Historic Trust archives provided data prior to 1890. Topographic maps and county census maps from the USGS archives provided a significant resource beginning in 1890. Digital land use and land cover maps derived by USGS provide data for 1972. Digital Line Graphs for transportation and hydrography provided a useful source of digital cartographic data. The Landsat Multispectral Scanner and Thematic Mapper provided a valuable source of multispectral images beginning in 1972 (Figure 6).
Figure 6. Images from the Landsat data archive assembled for the Baltimore-Washington regional study. The Landsat images record the extent of development near the Dulles Airport for 1972, 1982, and 1992. The lower images represent the urban delineations derived from the Landsat data.
The compilation of urban development was accomplished by a combination of manual photo-interpretation, map transcriptions, table digitizing, and automated spectral classification. The type of source materials available determined the actual compilation procedures that were used. Urban extent derived from topographic maps was compiled by map interpretation and delineation on mylar map overlays. Urban development derived from Landsat data was determined by digital image processing procedures that combined the spectral information from Landsat with road network density.
The San Francisco and Baltimore-Washington regional studies have successfully demonstrated the utility of integrating existing historic maps with remotely sensed data and related geographic information to dynamically map urban land characteristics for large metropolitan areas. These regional databases provide a strong visual portrayal of recognized growth patterns, and dramatically convey how the progress of modern urbanization results in profound changes to the landscape. Both the San Francisco Bay and the Baltimore-Washington datasets document the dramatic increase in urban development following World War II and continuing to the present day. The urban database focuses attention on the forces influencing the creation of the spatial patterns and corridors that have developed over time. The principal transportation data layer clearly demonstrates the influence that the transportation infrastructure (roads, railroads, and seaports) have exerted on population development. The hydrography data layer contributes to historical understanding by indicating the loss of navigable rivers by siltation, the development of reservoirs for water supplies, and the changing shoreline resulting from harbor development and salt pond formation.
This project demonstrates an advancement in the technology that can be applied to the study of spatial temporal dynamics related to human impacts on land transformation. This knowledge-base and technology must be strengthened to meet the goals of national programs such as NASA's Mission to Planet Earth (MTPE), the Global Change Research Program, environmental assessment and the growing interest in developing methods for sustainable development. This project has many facets, in particular, it provides a temporal dimension to the current USGS land use and land cover mapping activities. The project offers many additional opportunities, including a natural testbed for the national framework concepts in data integration and metadata specifications.
Public reaction to this experimental mapping work has been very positive and supportive of this innovative use of a temporal GIS database. The interest generated by applying modern mapping techniques to localized problems is understandable in terms of enhancing community support and should be used to justify continued federal agency support of a broader temporal mapping program of national and global urbanization phenomena.
Historical overviews of urban development provide insights into future development and expansion trends. This work will contribute to the research and technology base needed to understand the dynamics of urban phenomena. The database will be of much use to urban and regional planners, policy and decision makers, Earth scientists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution and sedimentation, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast future areas of urban growth.
This research has been a collaborative effort among many individuals. Special thanks to other members of the team especially, Janet Crawford and Susan Clark, USGS National Mapping Division, and Dana Hinzman, Walter Prince, and Helen Wiggins, University of Maryland Baltimore County. This work is sponsored in part by NASA Research Grant NAGW-1743.
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