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Modeling the History of the CityMichael C. Page a , Kimberly Durante a & Randy Gue aa Emory University , Atlanta , Georgia , USAPublished online: 17 Apr 2013.

To cite this article: Michael C. Page , Kimberly Durante & Randy Gue (2013) Modeling the History ofthe City, Journal of Map & Geography Libraries: Advances in Geospatial Information, Collections &Archives, 9:1-2, 128-139, DOI: 10.1080/15420353.2013.767763

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Journal of Map And Geography Libraries, 9:128–139, 2013Published with license by Taylor & FrancisISSN: 1542-0353 print / 1542-0361 onlineDOI: 10.1080/15420353.2013.767763

Modeling the History of the City

MICHAEL C. PAGE, KIMBERLY DURANTE, and RANDY GUEEmory University, Atlanta, Georgia, USA

Historical atlases and gazetteers have long proved invaluable toscholars examining phenomena with a space-time dynamic. Byleveraging geospatial technologies to link spatial features witharchive and library collections, historical atlases can be trans-formed into digital databases and tools for scholars and the public.“Modeling the History of the City” discusses the concept, methods,intended outcomes, and challenges of an ongoing project at EmoryUniversity Libraries in remapping early twentieth-century Atlanta.

KEYWORDS Atlanta, segregation, geocoding, geodatabase,geospatial data, GIS, metadata, spatial modeling

INTRODUCTION

Historical maps, atlases, and gazetteers are invaluable resources for schol-ars who examine phenomena with a space-time dynamic. Today’s geospa-tial technologies offer opportunities to link and transform spatial data frommanuscript collections and rare maps housed in special collections reposi-tories and other collections within libraries. Re-Mapping Segregated Atlantais a collaborative project between Emory University’s Robert W. WoodruffLibrary and its Manuscript, Archives, and Rare Book Library (MARBL) to cre-ate a series of interdisciplinary research tools to investigate the history ofsegregation in Atlanta. The project extracts and mines data from rare mapsand unique manuscript collections housed in MARBL and maps the data ina geographic information system (GIS), a system designed to capture, store,manipulate, and manage geographically referenced data. In particular, Re-Mapping Segregated Atlanta leverages geospatial technology to transform arare and historic atlas into a digital database and tool for students, faculty,scholars, and the general public.

© Michael C. Page, Kimberly Durante, and Randy GueAddress correspondence to Michael C. Page, Emory University, Emory Libraries, 540

Asbury Circle, Atlanta, GA 30322. E-mail: michael.page@emory.edu

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The initiative grew out of the question, How can students, faculty, andother researchers use library resources to study Jim Crow Atlanta? It is dif-ficult to analyze because much of the physical history of segregation hasdisappeared from the contemporary city’s landscape. The signs that reservedfacilities for a particular race were taken down, and most of the buildingswith separate entrances, balconies, restrooms, and water fountains were torndown decades ago. Although it is not visible in the city’s built environment,segregation left an impression on Atlanta and its topography. Re-MappingSegregated Atlanta develops tools to visualize and analyze segregation’s im-print on the city by creating ways to ask new questions and new ways tointegrate spatial and nonspatial data into research and the classroom. In par-ticular, the project will generate a Google Maps–like application, a geocoderfor historic Atlanta (circa 1930) that students and scholars can use to maptheir data sets, a geodatabase for historic Atlanta, and a base map of Atlantafor future projects. As an example, the project will map a large data set fromMARBL’s collection, the Hanley Bell Street Funeral Home records, to illustratethe power of visualizing data in this way. “Modeling the History of the City”presents a case study for planning and developing historical geodatabasesby using the Re-Mapping Segregated Atlanta project as a model.

THE ATLANTA ATLAS

The project began with a rare atlas entitled Atlas of Atlanta and Vicinity,1928 (Atlanta Mapping Division 1930) that was located in Emory University’sMARBL, the special collections repository for Emory Libraries. According toWorldCat, the global catalog of library and other institution’s collections,MARBL has the only cataloged copy of this atlas. The volume consists oftwo different sets of maps of Atlanta surveyed in 1928. Fifty of the volume’ssheets cover Atlanta’s central business district at the 1:200 scale, and the otherthirty-two maps survey the city’s suburban areas at 1:1000 scale. The mapsheets are full of rich details: topographic lines, latitude and longitude coor-dinates, administrative boundaries, survey benchmarks, hydrology, buildingfootprints, streets, railroad lines, and streetcar lines. The atlas identifies manypoints of interest including schools, parks, and churches as well as prisonsand the Fulton County Almshouse. The project leaders recognized that thesefeatures could be extracted and used as thematic layers in a GIS. The atlasand the data in it provided the technical foundation for this project to remapAtlanta.

The differences in scale of the atlas maps highlight why the late 1920swere such an interesting time in the development of Atlanta. The atlas cap-tures a detailed portrait of the city at a time when the definition of what themunicipality of Atlanta meant was changing and details the emergence ofmetropolitan Atlanta. After World War I, the expansion of suburbs beyond

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the edges of the central business district transformed land-use patterns andthe structure of the city. The Atlas of Atlanta and Vicinity, 1928 appropriatelyprovided the foundation for a long-term project because the structure of At-lanta did not change much for the next twenty years. The Great Depressionhalted residential and commercial construction, then the U.S. government’swartime restrictions on building materials meant that construction did notrebound until the late 1940s, when the restrictions were lifted.

The first step in creating this digital spatial history project was to digitizethe atlas using a large format, sheet-fed scanner. The resulting images weresaved in TIFF, an uncompressed tagged image file format. The images werepostprocessed in Adobe Photoshop and georeferenced with ESRI ArcGISDesktop using the reference information on the printed map. Once themaps were georeferenced, two spatial indexes were created as a data set.Both coverage and the indexes can be seen in Figure 1 as overlaid on 2009aerial photography of the Atlanta area (USDA/FSA 2011). With the permissionof the City of Atlanta, the map pages were then ingested into the MARBLDigital Historic Map Collection (available: http://digitalgallery.emory.edu),an online collection of printed maps and atlases related to Atlanta and theSouth. It features printed maps from MARBL’s collection published before1923, maps from books and atlases published before 1923, and a selectionof maps printed by state and local governments after 1923. The MARBLDigital Historic Map Collection used the LUNA Insight platform. LUNA allowsfor the discovery of imagery through simple and advanced search menuswithin its local user interface and provides metadata for indexing withinWeb search engines. LUNA also offers sophisticated zoom capability thatallows for detailed scrutiny of map features and accompanying text.

GEODATABASE DEVELOPMENT

Once the atlas was digitized, work began on creating a geodatabase forhistoric Atlanta based on the 1928 atlas. A GIS geodatabase is a spatialmodel of a landscape that includes descriptive and qualitative data aboutfeatures of the built and natural environments. It allows the project to storeand manage data about Atlanta such as roads, hydrography, and buildingsso it can be queried. In other words, the geodatabase provides the technicalinfrastructure for Re-Mapping Segregated Atlanta.

There were two stages in creating the geodatabase: design and devel-opment. In the design stage, the project leaders identified which thematiclayers would be extracted and digitized from the map sheets. The processwas complicated by the fact that each set of maps in the atlas contains adifferent legend; however, duplicating the legend was not appropriate giventhat the content of these legends varied based on the scale and the contentof the associated map sheet. The thematic features selected for extraction

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FIGURE 1 Indexes and geographic extent of atlas coverage, 2009 imagery, National Agricul-tural Imagery Program.

included building footprints, streets, alleys, sidewalks, fire hydrants, man-hole covers, rail lines, street car lines, administrative boundaries, surveybenchmarks, parks, and bodies of water. From this list, the project leadersdecided that building footprints, streets and alleys, and points of interest forlater classification were the most important features to extract first.

The geodatabase was designed using a large whiteboard wall at EmoryUniversity’s Robert W. Woodruff Library. The whiteboard wall was usedinstead of a computer for the first draft because the wall allowed the staff tovisualize the entire geodatabase and decide what should be added, deleted,or consolidated. The outline was then photographed and transcribed into acomputer-based format. Using ArcGIS Diagrammer (ArcGIS Resource Center

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2011), a computer application that creates a Microsoft Visio diagram, theschema then could be exported to ESRI’s ArcCatalog as a geodatabase modeltemplate. The resulting geodatabase schema is also exportable to the projectdocumentation as a diagram to explain the model and the associated rulesand relationships of the features. Now, both an empty template and a modelpopulated with data specifically for Atlanta can be distributed.

Once a model was produced, the next phase involved turning printedmap features into computer vector geometry. Automating the extraction pro-cess was attempted to turn the rasterized map scans into vectored featuresof geometric points, lines, and areas but was unsuccessful due to severalbarriers. At the 1:200 scale, the building footprints span across map sheetsin each direction and do not always line up with their adjacent counterparts.Further, the building illustrations were filled with parallel lines that were sub-ject to inconsistent fading due to age and use. Given these limitations andthe opportunity to use the project for teaching GIS to students, the decisionwas made to digitize or remap the features of the atlas.

Robert W. Woodruff Library’s Electronic Data Center began the processdigitizing footprints, streets, manholes, fire hydrants, railways, and bodies ofwater over the course of a year. At the 1:200 scale, building footprints arerepresented as polygon features and at the 1:1000 scale, they are representedas points. After all the polygons at the 1:200 scale were digitized, they wereconverted to polygon centroids, allowing us to combine them with the pointsof interest at the 1:1000 scale to create a complete point data set across bothscales for over 70,000 locations (see Figure 2). The next step to complete thisthematic layer will be to populate the attribute table with descriptive dataincluding information such as building use, occupant, race, date constructed,date demolished, and the number of floors.

The effort to digitize features allowed us to connect production withlearning opportunities for students. For example, a graduate student for adigital media class completed the electric streetcar layer and an undergrad-uate student in an introductory GIS course completed the hydrology layer.Although quality assurance, feature editing, and the development of topol-ogy rules still need to be addressed for these data layers, the students weregiven the opportunity to apply learning to production.

GEOCODING TOOLS

In the GIS community, address interpolators were devised to deal with theuncertainty of data. Address interpolation allows the batch conversion ofdata tables of street addresses into points on the map, often expressed inlongitude and latitude. In the case of the historical geocoder for Atlanta,the geometry for the street segments was edited to reflect the appropriatetopology, and address ranges were manually assigned. The finished address

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Modeling the History of the City 133

FIGURE 2 Two hundred-foot scale building footprints (polygons) and points of interest.

locator to be provided will allow users to batch process their address filesand display them on the base map that the geodatabase provides.

Key information sources consulted in the development of the historicalgeocoder were the Atlanta City Directories, a 1930 Atlanta House NumberingMap contributed by the University of Chicago, and for reference and datachecking, the Sanborn Map Collection of Atlanta. Using a Kirtas machine,Emory Libraries’ Digital Curation Center digitized the city directory. The

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FIGURE 3 Cluster in the center is where Interstates 75/85 converge with Interstate 20 justsouth of the central business district. Streets shown in orange are current streets as per theAtlanta Regional Commission. Red streets have been removed since 1928. Blue streets haveexisted since 1928. Green streets are unpaved roads or alleyways.

advertisements were then cropped using Adobe Photoshop and then pro-cessed using ABBYY Finereader that uses optical character recognition toconvert the image to text. Semiautomated text proofing was run twice inthe ABBYY Finereader and exported as a text file. Emory Libraries’ LewisH. Beck Center for Electronic Collections completed cleanup of the text andconverted it into a usable database organized by street blocks.

The street geometry was digitized and edited using ESRI’s ArcMap. Asshown in Figure 3, the streets symbolized in red are streets that have beenremoved since 1928, streets in blue existed in 1928 and present-day, andstreets in orange are current streets as represented in current data distributedby the Atlanta Regional Commission (ARC 2012). The streets shown in greenare unpaved roads and alleyways. In order to produce an address interpo-lator, the process of validating and defining each street segment’s geometryand address range is complex and time-consuming. Applying this process ina historical context can be problematic if there are data inconsistencies andlimitations to the geographic extents in the source information. Such is thecase with the source of data for Atlanta. Even though strategies like creatingaliases can address the challenges of data inconsistencies, the limitation ingeographic extent will likely reduce the coverage area of the address inter-polator. Therefore, in the case of Atlanta, priority was given to the absolutegeocoder.

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In an absolute geocoder, each point feature is assigned descriptive at-tributes, including address information. In searching the points-of-interestdatabase, the user queries the attribute table for matching names and ad-dresses. This type of geocoder will be useful in creating an online search toolfor single addresses and business or residential name searches not requiringthe complexity of the address interpolator. However, both the interpolatingand absolute geocoder will be used as a tool to create content for multipleAtlanta Studies scholarly-focused projects developed in the Emory UniversityLibraries Research Commons. The first of these will be the content createdusing the Hanley Bell data set.

HANLEY’S BELL STREET FUNERAL HOME RECORDS

Re-Mapping Segregated Atlanta needed a data set extracted from amanuscript collection to test the capability of the geocoder and to demon-strate how the project provided new ways to visualize the history of Atlanta.The project’s leaders identified the Hanley’s Bell Street Funeral Home recordsfrom MARBL as the most interesting collection and data set to extract andmap because the records shed new light on life in segregated Atlanta.

Hanley’s Bell Street Funeral Home was an African American funeralhome located just off Auburn Avenue in the middle of Atlanta’s historic blackcommunity. At this time, Atlantans were segregated—separated—in death asthey were in life; white undertakers prepared and buried white Atlantanswhile black undertakers prepared and buried Atlanta’s African Americans.The funeral home’s records contain death certificates for each body thecompany handled for each year between 1930 and 1954.

The individual certificates are filled with useful demographic data, in-cluding age, occupation, residence, and cause of death as well as informationabout church membership, fraternal organizations, and burial societies. The1930 ledger contains approximately 1,260 handwritten certificates, and eachcertificate has up to fifty data points that could potentially be stored in thedatabase. The resulting data set will become the first to test the batch geocod-ing capabilities of the historical geocoder, which will generate geographiccoordinates of each deceased’s residence. With this data, the project will,for example, be able to illustrate the connection—the correlation—betweensegregated housing patterns, topography, and incidence of disease.

METADATA FOR DIGITAL ASSETS

The integration of geospatial services into libraries has brought new con-siderations to the issue of data management within cross-disciplinary learn-ing environments. Spatial data is just one of several types of digital assets

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increasingly being managed through object-oriented repositories, which areoften integrated with enterprise tools such as library catalogs and electronicresource management systems. Architecting an interoperable, digital assetframework should rely strongly on standardized and appropriate levels ofmetadata in order to optimize strategies for long-term preservation, discov-ery, and access.

Compliance with open metadata standards will assist in the exchangeof structured data. This is of increasing importance, given the multiplicity offormats commonly incorporated in digital scholarship projects. Additionally,the capabilities of linked data applications, which rely on ontologies to clas-sify information from a broad range of knowledge bases, are supported bythe underlying, standardized metadata. Metadata can also play a key role inexpressing levels of associated services as well as defining rights for accessand use.

The objects created in the project represent a variety of source formatsincluding digitized paper maps and textual directories, aerial photographsand shapefiles, each with their own distinct features. The bound atlas wasdigitized and described at the page level using the Metadata Object De-scription Schema (MODS), generating a single record for each plate. Usingitem-level metadata that relate to the larger publication helps to frame theresource for discovery and contextual viewing within Web applications. Thisis a granular approach that embeds certain characteristics specific to theurban development of Atlanta such as businesses, churches, transportationnetworks, hospitals, and schools, into the data that describe the item.

The hierarchy enforced in the structure of the MODS schema allows forelements to express relational geography and other cartographic informationin the form of coordinates or projection specifications. MODS metadata istransformed into unqualified Dublin Core using Extensible Stylesheet Trans-formation. Although complex relationships, such as whole-part associations,are better expressed in the more granular MODS, the transformed DublinCore metadata should provide a metadata stream that satisfies the require-ments outlined in the Open Archives Initiative Protocol for Metadata Harvest-ing (OAIPMH), which allows for the servicing and harvesting of metadataacross extensible markup language (XML)-aware environments. Using stan-dardized and interoperable metadata structures has allowed for the XML filesand their metadata to be systematically loaded into the LUNA platform wherethey can be searched and displayed from within the application interface, oraccessed by way of search engines. Flexible metadata transformation allowsfor digital assets to be published as collections of specific interest, while stillclassifying them among other related material such as the David RumseyHistorical Maps Collection.

When considering metadata for managing digital data such as shapefiles,aerial photography, geodatabases, or tabular information, schemata such asMODS or Dublin Core may not satisfy the total set of requirements involved

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in the long-term provision of these resources. In the United States, the mostwidely used standard is the Content Standard for Digital Geospatial Metadata,more commonly known by the name of its author, the Federal GeographicData Committee (FGDC). A major impetus for the widespread adoption of thestandard was to normalize data for record description and reuse among muchbroader communities outside the academic sphere. However, the growingnecessity for data management plans will require researchers to considermetadata management as a critical step in their processes.

Identification information in an FGDC record should contain familiardescriptive elements such as author, title, publisher, and topic keywords.Additionally, records describing spatial data sets often require informationnecessary to evaluate and employ the resource. Elements detailing process,those that describe how the resource was compiled or created, will referencesoftware or source material necessary for understanding any issues withpotential refactoring of the data. It is also good practice to include a generalstatement of purpose describing why the data were created.

Because the Atlanta Maps Project focuses primarily on historical data,an appropriate FGDC record for relevant data sets should include informa-tion about the time period for which the data are considered to be accu-rate. Data relating to key pieces of urban infrastructure during a specificdate range will change over time, so using a valid date encoding schema,as well as keywords from specified temporal or place keyword thesauri,is recommended. Metadata should include references to a spatial domainspecifying the appropriate coordinate system and projection method in or-der to accurately view the data within Web mapping applications. Datasets often require metadata about any entity or attribute details to providedefinitions and values regarding any database elements that comprise theresource.

Unlike other digital assets such as images or text, spatial data sets oftenrequire that metadata be created during the development of the resource. Thedata producer is in many ways uniquely qualified to provide information onpertinent dates regarding publication, submission, subsequent modifications,and temporal coverage. However, the responsibility for metadata creation atthe FGDC level might often seem overwhelming to scholars, given the highlystructured array of information enforced by the standard. Libraries offeringdata services can assist in this arena by predesigning templates for record-ing of mandatory elements and assisting with designating levels of rights foraccess and use. FGDC metadata for much of Emory University’s researchdata are created using ArcCatalog and loaded into the libraries’ geospatialWeb catalog using an XML upload mechanism. Place names from the Geo-graphic Names Information System are assigned along with geographic andtopical headings from the Library of Congress subject headings thesaurus,and the appropriate contact information is provided in case the data cannotbe accessed online.

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The importance of standards-based metadata cannot be emphasizedenough because the linking and exchange potentials of the Web continueto grow. Adhering to well-documented, published standards will help facil-itate interoperability within both local environments and in the larger Webinfrastructure. This approach to metadata management for academic librariesin particular will assist in the stewardship of unique resources that can bepublicized and shared with the international community.

CONCLUSION

Change is a feature of urban landscapes that urban scholars and histori-ans face constantly. Neighborhoods, businesses, office buildings, churches,schools, and roads appear and disappear regularly. Modeling the city in ahistorical context gives us the opportunity to explore, classify, and measureit in new ways. The authors of this paper recognize the importance of pro-viding such a model to the academic community as a resource for otherproject designers wanting to model the historical landscape of a city usinghistorical topographic maps. Therefore, the Atlanta historical geodatabasewill be offered as an exemplary geodatabase schema with accompanyingproject documentation, and the thematic GIS layers will be released as theyare completed, reviewed, and approved.

Building a research platform that supports data visualization for bothprimary and derived information sources will require the expertise of severalknowledge domain experts. In an environment that is becoming increas-ingly interdisciplinary, the implementation of key technologies that powerdigital scholarship activities should take into account the multiplicity of re-search questions as well as any potential future uses of the information. Asthe remapping of segregated Atlanta advanced, the question emerged: Howcan students, faculty and other researchers use library resources to studysegregated Atlanta? Through a modeling of the history of Atlanta, we willhave new opportunities for—and insight into—exploring this and future, yetunimagined, questions.

REFERENCES

“ArcGIS Diagrammer,” ArcGIS Resource Center, http://resources.arcgis.com/gallery/file/arcobjects-net-api/details?entryID=F12ADF8F-1422-2418-34B2-C276C6BCCF98, last modified April 29, 2011.

“ARC Regional GIS Data,” Atlanta Regional Commission (ARC), http://www.atlantaregional.com/info-center/gis-data-maps/gis-data, last modified August14, 2012.

Atlanta Mapping Division, Construction Department. Atlas of Atlanta and Vicinity,1928. S.P. Floore, topographic engineer in charge/topography by C.A. Killian

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