Adding value to digitizing with GIS
Identifieur interne : 001266 ( Istex/Corpus ); précédent : 001265; suivant : 001267Adding value to digitizing with GIS
Auteurs : Marianne Stowell Bracke ; C. C. Miller ; Jae KimSource :
- Library Hi Tech [ 0737-8831 ] ; 2008-06-13.
Abstract
Purpose The purpose of this paper is to present a project that digitized the 1906 Soil Survey of Tippecanoe County, Indiana, extracted its contents into fulltext and geospatial datasets, and then built them into a web application designed to approximate but improve upon the way soil surveys are typically used by soil scientists in their research and field work. Designmethodologyapproach The components of a 1906 soil survey document were scanned and their contents were extracted using several different methods, chief among them imagery segmentation and classification. The resulting datasets included a fulltext version of the original narrative and two georeferenced versions of the soil survey map. Findings Going several steps beyond just scanning, including the application of geographic information system GIS capabilities, adds significant value to geospatial materials whose contents are still relevant but whose formats are cumbersome. In addition, this allows for a GIS platform to which other maps and content can be added. Originalityvalue This is a unique approach to enhancing content through GIS.
Url:
DOI: 10.1108/07378830810880315
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ISTEX:02D7534BF610E0F313A130657F1D7898C1843106Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Adding value to digitizing with GIS</title><author><name sortKey="Stowell Bracke, Marianne" sort="Stowell Bracke, Marianne" uniqKey="Stowell Bracke M" first="Marianne" last="Stowell Bracke">Marianne Stowell Bracke</name><affiliation><mods:affiliation>Purdue University Libraries LIFE, West Lafayette, Indiana, USA</mods:affiliation></affiliation></author><author><name sortKey="Miller, C C" sort="Miller, C C" uniqKey="Miller C" first="C. C." last="Miller">C. C. Miller</name><affiliation><mods:affiliation>Purdue University Libraries EAS Library, West Lafayette, Indiana, USA</mods:affiliation></affiliation></author><author><name sortKey="Kim, Jae" sort="Kim, Jae" uniqKey="Kim J" first="Jae" last="Kim">Jae Kim</name><affiliation><mods:affiliation>Geomatics Engineering, Purdue University, West Lafayette, Indiana, USA</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:02D7534BF610E0F313A130657F1D7898C1843106</idno><date when="2008" year="2008">2008</date><idno type="doi">10.1108/07378830810880315</idno><idno type="url">https://api.istex.fr/document/02D7534BF610E0F313A130657F1D7898C1843106/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001266</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Adding value to digitizing with GIS</title><author><name sortKey="Stowell Bracke, Marianne" sort="Stowell Bracke, Marianne" uniqKey="Stowell Bracke M" first="Marianne" last="Stowell Bracke">Marianne Stowell Bracke</name><affiliation><mods:affiliation>Purdue University Libraries LIFE, West Lafayette, Indiana, USA</mods:affiliation></affiliation></author><author><name sortKey="Miller, C C" sort="Miller, C C" uniqKey="Miller C" first="C. C." last="Miller">C. C. Miller</name><affiliation><mods:affiliation>Purdue University Libraries EAS Library, West Lafayette, Indiana, USA</mods:affiliation></affiliation></author><author><name sortKey="Kim, Jae" sort="Kim, Jae" uniqKey="Kim J" first="Jae" last="Kim">Jae Kim</name><affiliation><mods:affiliation>Geomatics Engineering, Purdue University, West Lafayette, Indiana, USA</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Library Hi Tech</title><idno type="ISSN">0737-8831</idno><imprint><publisher>Emerald Group Publishing Limited</publisher><date type="published" when="2008-06-13">2008-06-13</date><biblScope unit="volume">26</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="201">201</biblScope><biblScope unit="page" to="212">212</biblScope></imprint><idno type="ISSN">0737-8831</idno></series><idno type="istex">02D7534BF610E0F313A130657F1D7898C1843106</idno><idno type="DOI">10.1108/07378830810880315</idno><idno type="filenameID">2380260204</idno><idno type="original-pdf">2380260204.pdf</idno><idno type="href">07378830810880315.pdf</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0737-8831</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Purpose The purpose of this paper is to present a project that digitized the 1906 Soil Survey of Tippecanoe County, Indiana, extracted its contents into fulltext and geospatial datasets, and then built them into a web application designed to approximate but improve upon the way soil surveys are typically used by soil scientists in their research and field work. Designmethodologyapproach The components of a 1906 soil survey document were scanned and their contents were extracted using several different methods, chief among them imagery segmentation and classification. The resulting datasets included a fulltext version of the original narrative and two georeferenced versions of the soil survey map. Findings Going several steps beyond just scanning, including the application of geographic information system GIS capabilities, adds significant value to geospatial materials whose contents are still relevant but whose formats are cumbersome. In addition, this allows for a GIS platform to which other maps and content can be added. Originalityvalue This is a unique approach to enhancing content through GIS.</div></front></TEI><istex><corpusName>emerald</corpusName><author><json:item><name>Marianne Stowell Bracke</name><affiliations><json:string>Purdue University Libraries LIFE, West Lafayette, Indiana, USA</json:string></affiliations></json:item><json:item><name>C.C. Miller</name><affiliations><json:string>Purdue University Libraries EAS Library, West Lafayette, Indiana, USA</json:string></affiliations></json:item><json:item><name>Jae Kim</name><affiliations><json:string>Geomatics Engineering, Purdue University, West Lafayette, Indiana, USA</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Geographic information systems</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Soil surveys</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Agronomy</value></json:item></subject><language><json:string>eng</json:string></language><abstract>Purpose The purpose of this paper is to present a project that digitized the 1906 Soil Survey of Tippecanoe County, Indiana, extracted its contents into fulltext and geospatial datasets, and then built them into a web application designed to approximate but improve upon the way soil surveys are typically used by soil scientists in their research and field work. Designmethodologyapproach The components of a 1906 soil survey document were scanned and their contents were extracted using several different methods, chief among them imagery segmentation and classification. The resulting datasets included a fulltext version of the original narrative and two georeferenced versions of the soil survey map. Findings Going several steps beyond just scanning, including the application of geographic information system GIS capabilities, adds significant value to geospatial materials whose contents are still relevant but whose formats are cumbersome. In addition, this allows for a GIS platform to which other maps and content can be added. Originalityvalue This is a unique approach to enhancing content through GIS.</abstract><qualityIndicators><score>6.992</score><pdfVersion>1.3</pdfVersion><pdfPageSize>519 x 680 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>3</keywordCount><abstractCharCount>1115</abstractCharCount><pdfWordCount>5051</pdfWordCount><pdfCharCount>29637</pdfCharCount><pdfPageCount>12</pdfPageCount><abstractWordCount>166</abstractWordCount></qualityIndicators><title>Adding value to digitizing with GIS</title><genre.original><json:string>e-technical-paper</json:string></genre.original><genre><json:string>other</json:string></genre><host><volume>26</volume><publisherId><json:string>lht</json:string></publisherId><pages><last>212</last><first>201</first></pages><issn><json:string>0737-8831</json:string></issn><issue>2</issue><subject><json:item><value>Information & knowledge management</value></json:item><json:item><value>Information & communications technology</value></json:item><json:item><value>Internet</value></json:item><json:item><value>Library & information science</value></json:item><json:item><value>Information behaviour & retrieval</value></json:item><json:item><value>Librarianship/library management</value></json:item><json:item><value>Information user studies</value></json:item><json:item><value>Metadata</value></json:item><json:item><value>Library technology</value></json:item></subject><genre><json:string>Journal</json:string></genre><language><json:string>unknown</json:string></language><title>Library Hi Tech</title><doi><json:string>10.1108/lht</json:string></doi></host><publicationDate>2008</publicationDate><copyrightDate>2008</copyrightDate><doi><json:string>10.1108/07378830810880315</json:string></doi><id>02D7534BF610E0F313A130657F1D7898C1843106</id><fulltext><json:item><original>true</original><mimetype>application/pdf</mimetype><extension>pdf</extension><uri>https://api.istex.fr/document/02D7534BF610E0F313A130657F1D7898C1843106/fulltext/pdf</uri></json:item><json:item><original>false</original><mimetype>application/zip</mimetype><extension>zip</extension><uri>https://api.istex.fr/document/02D7534BF610E0F313A130657F1D7898C1843106/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/02D7534BF610E0F313A130657F1D7898C1843106/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Adding value to digitizing with GIS</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Emerald Group Publishing Limited</publisher><availability><p>EMERALD</p></availability><date>2008</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Adding value to digitizing with GIS</title><author><persName><forename type="first">Marianne</forename><surname>Stowell Bracke</surname></persName><affiliation>Purdue University Libraries LIFE, West Lafayette, Indiana, USA</affiliation></author><author><persName><forename type="first">C.C.</forename><surname>Miller</surname></persName><affiliation>Purdue University Libraries EAS Library, West Lafayette, Indiana, USA</affiliation></author><author><persName><forename type="first">Jae</forename><surname>Kim</surname></persName><affiliation>Geomatics Engineering, Purdue University, West Lafayette, Indiana, USA</affiliation></author></analytic><monogr><title level="j">Library Hi Tech</title><idno type="pISSN">0737-8831</idno><idno type="DOI">10.1108/lht</idno><imprint><publisher>Emerald Group Publishing Limited</publisher><date type="published" when="2008-06-13"></date><biblScope unit="volume">26</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="201">201</biblScope><biblScope unit="page" to="212">212</biblScope></imprint></monogr><idno type="istex">02D7534BF610E0F313A130657F1D7898C1843106</idno><idno type="DOI">10.1108/07378830810880315</idno><idno type="filenameID">2380260204</idno><idno type="original-pdf">2380260204.pdf</idno><idno type="href">07378830810880315.pdf</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2008</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Purpose The purpose of this paper is to present a project that digitized the 1906 Soil Survey of Tippecanoe County, Indiana, extracted its contents into fulltext and geospatial datasets, and then built them into a web application designed to approximate but improve upon the way soil surveys are typically used by soil scientists in their research and field work. Designmethodologyapproach The components of a 1906 soil survey document were scanned and their contents were extracted using several different methods, chief among them imagery segmentation and classification. The resulting datasets included a fulltext version of the original narrative and two georeferenced versions of the soil survey map. Findings Going several steps beyond just scanning, including the application of geographic information system GIS capabilities, adds significant value to geospatial materials whose contents are still relevant but whose formats are cumbersome. In addition, this allows for a GIS platform to which other maps and content can be added. 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Do Not Edit! --><article dtd-version="1.0" xml:lang="en" article-type="e-technical-paper"><front><journal-meta><journal-id journal-id-type="publisher-id">lht</journal-id><journal-id journal-id-type="doi">10.1108/lht</journal-id><journal-title-group><journal-title>Library Hi Tech</journal-title></journal-title-group><issn pub-type="ppub">0737-8831</issn><publisher><publisher-name>Emerald Group Publishing Limited</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.1108/07378830810880315</article-id><article-id pub-id-type="original-pdf">2380260204.pdf</article-id><article-id pub-id-type="filename">2380260204</article-id><article-categories><subj-group subj-group-type="type-of-publication"><compound-subject><compound-subject-part content-type="code">e-technical-paper</compound-subject-part><compound-subject-part content-type="label">Technical paper</compound-subject-part></compound-subject></subj-group><subj-group subj-group-type="subject"><compound-subject><compound-subject-part content-type="code">cat-IKM</compound-subject-part><compound-subject-part content-type="label">Information & knowledge management</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-ICT</compound-subject-part><compound-subject-part content-type="label">Information & communications technology</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-INT</compound-subject-part><compound-subject-part content-type="label">Internet</compound-subject-part></compound-subject></subj-group></subj-group></subj-group><subj-group subj-group-type="subject"><compound-subject><compound-subject-part content-type="code">cat-LISC</compound-subject-part><compound-subject-part content-type="label">Library & information science</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-IBRT</compound-subject-part><compound-subject-part content-type="label">Information behaviour & retrieval</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-IUS</compound-subject-part><compound-subject-part content-type="label">Information user studies</compound-subject-part></compound-subject><compound-subject><compound-subject-part content-type="code">cat-MTD</compound-subject-part><compound-subject-part content-type="label">Metadata</compound-subject-part></compound-subject></subj-group></subj-group><subj-group><compound-subject><compound-subject-part content-type="code">cat-LLM</compound-subject-part><compound-subject-part content-type="label">Librarianship/library management</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-LTC</compound-subject-part><compound-subject-part content-type="label">Library technology</compound-subject-part></compound-subject></subj-group></subj-group></subj-group></article-categories><title-group><article-title>Adding value to digitizing with GIS</article-title></title-group><contrib-group><contrib contrib-type="author"><string-name><given-names>Marianne</given-names> <surname>Stowell Bracke</surname></string-name><aff>Purdue University Libraries – LIFE, West Lafayette, Indiana, USA</aff></contrib><x></x><contrib contrib-type="author"><string-name><given-names>C.C.</given-names> <surname>Miller</surname></string-name><aff>Purdue University Libraries – EAS Library, West Lafayette, Indiana, USA</aff></contrib><x></x><contrib contrib-type="author"><string-name><given-names>Jae</given-names> <surname>Kim</surname></string-name><aff>Geomatics Engineering, Purdue University, West Lafayette, Indiana, USA</aff></contrib></contrib-group><pub-date pub-type="ppub"><day>13</day><month>06</month><year>2008</year></pub-date><volume>26</volume><issue>2</issue><fpage>201</fpage><lpage>212</lpage><permissions><copyright-statement>© Emerald Group Publishing Limited</copyright-statement><copyright-year>2008</copyright-year><license license-type="publisher"><license-p></license-p></license></permissions><self-uri content-type="pdf" xlink:href="07378830810880315.pdf"></self-uri><abstract><sec><title content-type="abstract-heading">Purpose</title><x> – </x><p>The purpose of this paper is to present a project that digitized the 1906 Soil Survey of Tippecanoe County, Indiana, extracted its contents into full‐text and geospatial datasets, and then built them into a web application designed to approximate but improve upon the way soil surveys are typically used by soil scientists in their research and field work.</p></sec><sec><title content-type="abstract-heading">Design/methodology/approach</title><x> – </x><p>The components of a 1906 soil survey document were scanned and their contents were extracted using several different methods, chief among them imagery segmentation and classification. The resulting datasets included a full‐text version of the original narrative and two georeferenced versions of the soil survey map.</p></sec><sec><title content-type="abstract-heading">Findings</title><x> – </x><p>Going several steps beyond just scanning, including the application of geographic information system (GIS) capabilities, adds significant value to geospatial materials whose contents are still relevant but whose formats are cumbersome. In addition, this allows for a GIS platform to which other maps and content can be added.</p></sec><sec><title content-type="abstract-heading">Originality/value</title><x> – </x><p>This is a unique approach to enhancing content through GIS.</p></sec></abstract><kwd-group><kwd>Geographic information systems</kwd><x>, </x><kwd>Soil surveys</kwd><x>, </x><kwd>Agronomy</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>peer-reviewed</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>academic-content</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>rightslink</meta-name><meta-value>included</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec><title>Introduction</title><p>In November 2007 Purdue University's Agronomy Department will celebrate its centennial anniversary. A number of events and programs are planned that will revisit the history of agronomy, including Purdue's contribution, while promoting some understanding and awareness of the current state and future of the field. Agronomy has changed much over the last 100 years through the use of progressively more sophisticated technology and the increases in the understanding of soil properties. Purdue Libraries will support the Agronomy celebration in a number of ways, but the focus of this paper is a project that digitized the 1906 Soil Survey of Tippecanoe County, Indiana, extracted its contents into full‐text and geospatial datasets, and then built them into a web application designed to approximate but improve upon the way soil surveys are typically used by soil scientists in their research and field work. The result is a mashup, of sorts, that pulls together pre‐war analog agronomy and twenty‐first century digital techniques and technologies.</p><p>This paper will first provide some context for soil surveys, including their importance to soil scientists and agronomists as well as the nature of the publications themselves. Following this it will outline the challenges and charges of the project, its ultimate goal, and the trajectory of the work by Purdue Libraries. This work includes the process developed and the architecture and functionality of the alpha pre‐release of the online application. Finally, it will include a section that discusses future directions for and potential impacts of, the project, including some reflection about where this project fits into novel developments in interdisciplinary librarianship such as GIS.</p></sec><sec><title>Soil and soil surveys</title><p>Soil is more than the dirt beneath our feet. It is a living membrane around the earth and features a complex ecosystem of mineral and organic matter, liquids, and gasses. A type of soil and its geographic location is important information to farmers, engineers, land developers and agronomists, among others, as they plan current and future uses of the land. The physical, chemical and biological properties of soil vary highly across the spatial extent of even small regions. Soil also varies downward, through co‐reliant strata that result from changes in the earth over long periods of time. The ability of soils to tell epic stories about the earth has perhaps never been so important as in the last century, as this country experienced dramatic shifts in land use from the rapid growth of cities, the decline of family farming, greater productivity through intense soil management practices, and the rise of massive commercial agriculture (<xref ref-type="bibr" rid="b12">Richter and Markewitz, 2001</xref>).</p><p>Soil surveys are very detailed reports that describe the physical and chemical properties of the soil over a given area, typically a county. Surveys are usually published for each county every 40‐50 years, the interval at which soils are re‐examined and sampled. As detailed catalogs of the telling elements of soil, soil surveys are an important tool in understanding the changes in and current status of our earth and the activities that engage and alter it. Surveying began in the USA in the last part of the 19th century, at a time when the surveying was a very manual and time‐consuming endeavor. Surveyors walked, boated, rode horses or buggies or chuck wagons or bicycles into the field to gather data with a heavy complement of plane tables, augers, picks and shovels. New techniques were added over the years, among them the use of aerial photography for soil mapping, that greatly improved the accuracy and level of detail and sped up the surveying process itself (<xref ref-type="bibr" rid="b7"><italic>History of Soil Survey</italic>, 1999</xref>).</p><p>Survey publications present information in a variety of ways, including narrative descriptions of the area and soil types; suggested use and management of soils; and interpretive tables, aerial photography, and maps depicting the zones within a county dominated by a given soil type. Over the past century, as soil knowledge has expanded and surveying technology has improved, soil surveys have become more sophisticated with longer text and greater numbers of photos and maps. The 1906 Tippecanoe County Soil Survey, for example, is 40 pages long with a single map. The updated survey of 1959 is 117 pages with 13 maps. By 1998, the Tippecanoe survey ballooned to 350 pages with 84 maps.</p><p>The National Resources Conservation Service (NRCS), a child of the United States Department of Agriculture (USDA), publishes the surveys and coordinates data collection across the country. Although NRCS' goal is to publish all parts of the survey online, new surveys are being published in part online and in print (<xref ref-type="bibr" rid="b5">GPO Listserv, 2007</xref>). Online materials have the added benefit of a sophisticated array of web tools to allow researchers to view modern soil data in numerous ways. Furthermore, data can be downloaded to desktops or to rugged tablet PCs and used in the field.</p></sec><sec><title>Why reclaim historic surveys?</title><p>Soil surveys tell stories with multiple timelines. Any one publication catalogs the properties of the soil in one county for a given time, but taken collectively surveys can illustrate how glacial action thousands of years ago changed the soil, as great layers of ice compressed the earth beneath then displaced and dispersed silt and sand as it melted. Looking at historical data of even the past 100 years, researchers can see how and where the soil has changed, a useful tactic in predicting how certain interactions with the land – farming, civic development, recreational – might affect the soil in the future. But there is the rub: looking at data from 100 years ago is more difficult than it sounds. Most work with soil is now done with electronic data, and even if one wanted to use the 1906 survey in their work, they necessarily must use fragile, brittling, analog materials and certainly at some stage do some manner of digitization. The Libraries felt that the unique combination of traditional text and map data made the soil surveys especially good candidates for a fully digital resurrection, one that could not only make the data available to agronomists electronically, but also boost the discovery and consumption of them by a more general audience.</p><p>Because of the continued usefulness of soil data, it was important to extend the life of this project beyond the anniversary celebration itself. To that end, this project intends to provide access to, preserve, add value to, and expose to the modern research workflow a collection of materials that have much to say about soil, land use, human development, husbandry, and a collective understanding of the land. Unfortunately, the use of these materials had atrophied as they became increasingly dusty, forgotten analogs of more modern data. By resurrecting the materials into a rich, multi‐functional web environment rather than one monolithic PDF file that perfectly duplicated the original, the materials could be introduced into the research workflow and the document's functionality could be constructed with specific uses in mind.</p></sec><sec><title>Methodology: personnel and narrative phase</title><p>The Libraries team for this project is led by the Agricultural Sciences Information Specialist and the Geographic Information Systems (GIS) Specialist. They are supported by two of the GIS Specialist's graduate students (map digitization) and members of the Libraries' Archives and Special Collections faculty and staff (scanning and metadata). The first task for this team was to learn just how soil surveys are usually consumed. Conversations with an <italic>ad hoc</italic> council of Purdue agronomists revealed that survey documents are typically used map‐first. That is, one locates an area of interest on the map to discover which soil type(s) cover that region, and then – armed with a given soil type or types – returns to the narrative portion of the document to investigate those soils. The narrative portion of the document is organized into chapters by soil class. Each soil in the county gets a chapter in which its characteristics and suitability for different uses are discussed with occasional asides for statistics, charts, images and comparisons to other soils. The map, naturally, is organized spatially. Though there are incidental elements including roads, political boundaries, and survey grids, the chief attribute of the map is its continuous surface of soil zones, each of which matches one of the soil classes that form the backbone of the narrative. In order to link these two document components together, at the very least that shared element, the soil class, needed to be fully exposed.</p><p>For the narrative portion, the process of exposing the soil classes used common digitization practices: the pages were scanned at 600 dpi and ingested into the Libraries' institutional repository, a CONTENTdm installation, where it was made available and accessible via human‐written qualified Dublin Core metadata and a full‐text index generated by a round of OCR, and with several CONTENTdm gadgets such as query term highlighting, resolution scaling, and PDF export (<ext-link ext-link-type="uri" xlink:href="http://earchives.lib.purdue.edu/u?/TippeSoil,126">http://earchives.lib.purdue.edu/u?/TippeSoil,126</ext-link>).</p></sec><sec><title>Methodology: map phase</title><p>Exposing soil classes on the map was a much more involved operation, as the target unit was not a typeset string of text but a highly‐variable geometric shape, drawn by hand in 1906 and available now on a yellowed, 24″×30″ page. The following section will overview and elaborate on the process used to extract spatially‐aware, classified soil data from this original analog map sheet.</p></sec><sec><title>Methodology: map phase: georeferencing</title><p>Although the scan of the text portion of the document was performed using Purdue's own copy of the Tippecanoe survey, that document's map had been used to a point of disrepair, and the team kindly thanks the University of Illinois at Champaign‐Urbana for loaning Purdue Libraries their copy of the map for scanning. This scan – at 600 dpi and 32‐bit color – generated an uncompressed.tif of ∼730 MB. This image was then loaded into ESRI's ArcInfo GIS for georeferencing. Georeferencing – the process of taking raw image data and manually fixing it to its proper place in some known geographic coordinate system – was by far the most time‐consuming portion of the process. It did, however, enable what is arguably the most useful result of the project: the ability to import these old data into a modern GIS, perfectly overlaying or overlayed by modern iterations of soil or other data. To complete the georeferencing process, ArcGIS's “Adjust“ algorithm was used, which combines polynomial transformations and Triangulated Irregular Network (TIN) interpolation in an attempt at both global and local accuracy (<ext-link ext-link-type="uri" xlink:href="http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?id=2698&pid=2689&topicname=Georeferencing_a_raster_dataset">http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?id=2698&pid=2689&topicname=Georeferencing_a_raster_dataset</ext-link>). In total, 330 control points were placed during this process (locations on the soil image that correspond to identifiable locations a map with established accuracy) that resulted in a Root Mean Square (RMS) error – a measure of difference between known and unknown locations – of zero. It is useful to reiterate here that “accuracy” is a more nebulous concept for soil data than it is for other types such as a GPS tracklog or even road data. Soils on maps are represented as edged, non‐overlapping polygons which are themselves estimates based on spot samples. Real soil is never so finite, of course, so there is a certain amount of fuzziness and interpolation built into a soil datum to begin with which is further exacerbated by the crudity of 1906 instrumentation and understanding. In other words, getting the 1906 map to line up perfectly with modern map data is no real favor to the true accuracy of the resulting digital map. This is a useful self‐critique to note, however, as reducing the time spent placing control points during the rectification process will significantly reduce the amount of time it takes to process future documents. That said, mapping, surveying, and other soil survey capabilities improved the accuracy of future generations of these publications, and it does follow logically that the digitization of later‐model maps might benefit from or even require the more rigorous attention to the accuracy of digitization practiced this time.</p></sec><sec><title>Methodology: map phase: digitizing</title><p>Once the map was georeferenced, a copy of the raster image was stored away as a completed GeoTIFF dataset that will ultimately be stored in an ArcSDE database and made available for full‐resolution download within the final public interface. The next step was to extract from the image those shapes that depict different zones of soil. Very generally, the process requires the identification of pixels that belong together, the grouping of these pixels into classes within software memory, then finally the creation of a new file with these finalized classes intact and in a vector, rather than raster, format. Traditionally, the identification of polygon classes out of flat raster imagery is accomplished by assigning each pixel to a class based on its color value. The 1906 Tippecanoe soil map, though beautiful, is somewhat cartographically complex, representing otherwise simple shapes with an artistic flair that complicates a computerized interpretation. For example, what looks generally like a blue river to the human eye on the map is actually a hodge‐podge of hundreds of colors, some of them not at all blue, spread across thousands of pixels. While the ability of the human eye to normalize and generalize and interpret a collection of colors allows us to automatically classify the shapes of the map, the rigid attention of a computer to the real RGB value of a pixel means it must be trained to abstract these collections of color into the shapes they are meant to represent. Borrowing a solution for similar problems with pixel‐based classification during high‐resolution post‐processing of remote sensing and other high‐quality modern imagery, the team used supervised object‐based classification to identify and extract soil zones (<xref ref-type="bibr" rid="b13">Tso and Mather, 2001</xref>). Object‐based classification uses segmentation, which is a software‐driven process that identifies homogenous regions in an image based on several pixel and pixel context criteria. As such, it has the ability to normalize the micro‐local variation of the data in favor of the more abstract collective of the map's major colors (the forest for the trees, so to speak).</p><p>This segmentation was performed in Definiens Professional (formerly eCognition), a proprietary software by Definiens for which Purdue University already had licensed seats available but that was still selected only after a scan for a highly‐usable open source alternative failed. Using Definiens Professional, project staff were able to efficiently and fluidly classify (and reclassify) microscopically heterogeneous, but macroscopically homogenous map colors into representations of the elements on the original. Naturally, the bulk of these elements were the individual soil zones, but other map elements were collected into classes as well, including roads, map grids, annotations and titles, and other surface elements such as hatching for drastic topography and rather artistic renderings of water bodies. These non‐soil elements became the “black classes,” an important group of colors to identify since they occupied map space that, in reality, belonged to a particular soil class. For instance, on the map, the black ink used to draw a letter T or the word “railroad” displaced the color used to represent the soil sitting under that text. Identifying these unnatural black classes meant all non‐soil elements could be removed en masse. The resulting gaps could then be filled in more or less automatically using a procedure that will be discussed later in this paper (see <xref ref-type="fig" rid="F_2380260204001">Figure 1</xref>).</p><p>The presence of the black classes necessarily affected the parameters given to and guessed by the segmentation process. Segmentation in Definiens Professional is an iterative, heuristic image optimization algorithm that considers several input parameters in an attempt to minimize local heterogeneity in favor of identifying more logical, higher‐order homogenous objects (<xref ref-type="bibr" rid="b4">Definiens Professional, 2006</xref>). Scale is arguably the most important of these parameters, as the growth of any object (“growth” here means the gathering of pixel after pixel until it is determined that the last pixel of a given class has been reached and the next one belongs to a different class) stops if it exceeds the scale input parameter. Several digitizations were attempted with several sets of input parameters before a setting was found that best captured both the soil zones and the black classes (see <xref ref-type="fig" rid="F_2380260204002">Figure 2</xref>).</p><p>The critical step in the segmentation process was classification, wherein samples of map data were manually added to or subtracted from each temporary soil classification in order to train the software's interpretation of the map data. Not only was this an important step toward a more accurate final result (where vector soil polygons equate very closely to the original image's soil zones), it also provided the most return on invested time. Taking care and having the patience to train Definiens Pro well at this stage helped it generate a vector version of the map that had the most efficient combination of smoothed, encompassing polygons (large, continuous soil zones that “swallow up“ very small, neighboring patches that are in truth of the same soil type but which appear slightly differently on the scan due to the vagaries of hand‐drawing a map or from blotches, creases in the paper, etc.) and small, but distinct patches of soil that are indeed supposed to be separate from larger neighbors. In fact the total accuracy report for the map following classification was 99.48 percent, with all classes at or above 97 percent save two at 93 percent and 76 percent, respectively.</p><p>With a sufficiently accurate segmentation and classification in hand, the interpreted image was exported out of Definiens Professional as an ESRI Shapefile (vector) and immediately imported into a file‐based geodatabase, a database format newly‐available with the 9.2 release of ESRI's ArcGIS. Even this was an intermediate step, taken in order to: allow the smoothing and generalization of polygon edges; and benefit from the application of geodatabase topologies to the map data. Topologies in ArcGIS let users establish rules for, and relationships between, data and were intended to solve the problem of the black classes: where black classes once appeared on the original map image there were now gaps in the vector coverage of the entire county. Worse still, these black gaps appeared indiscriminately throughout the map, meaning they caused polygons of the same type of soil to be split in half and they created gaps of unknown data (and thus unknown soil type) on the borders of two different soil classes. It was determined that a geodatabase topology would be the most efficient way to fill in those gaps (see <xref ref-type="fig" rid="F_2380260204003">Figure 3</xref>).</p><p>The topology was established using a cluster tolerance parameter (i.e. how close do features need to be in order to be included in the rule?) of 25m and one topological rule. Too much cluster tolerance and the carefully‐segmented polygons get fairly routinely warped; too little and almost as many gaps between the soil polygons would remain as before the process started. The one, lonely rule dictated that the soil polygons “Must Not Have Gaps”. Validation of the rule generated an inventory of every blank space in the entire dataset, which counted well into the 400s. Having to fix these gaps by hand would have legitimately derailed the viability of this project, and this gloomy prospect warranted an investigation of several ways to automate the process of closing the gaps between polygons left behind by the removal of the black classes. Ultimately the best way was to have ArcGIS force fix the errors by filling in gaps with additional polygons. Where two polygons had not met to share a border, for example, a new, filler, shape would be inserted to close up the surface. In this way, the quite highly‐accurate borders of the polygons generated out of segmentation would be preserved. However, the filler polygons had no class, as it made no sense to assign a default soil class to these new shapes upon their creation since there was no way to predict to which of their neighboring soil classes the new shapes belonged.</p><p>To assign soil classes to these filler objects, the filler shapes were converted into points, each of which represented a filler object's centroid. These centroids were then able to act as vessels, in a way, of soil class attributes. A spatial join transferred to each point the soil class of the polygon to which it was closest. Another spatial join then transferred that attribute to the filler polygon from which the point was derived. Assigning the class of the nearest polygon is not a foolproof way to accurately identify what soil class was actually displaced by the black classes, but a manual spot‐check check of those newly‐reclassified filler polygons against the original map found that a great majority were right.</p><p>One last run over the map with a human eye helped to properly classify misclassified filler polygons and fix small errors or anomalies in the original soil polygons (mostly border jags where a road or letter had distorted the edge). All disjointed polygons of the same soil class were then dissolved into whole, but multipart, polygons. At that point the soil extraction was finished, FGDC metadata were written, and everything was moved to a PostGIS database (PostgreSQL with geospatial capabilities) to be drawn out and rendered by a geodata server.</p><p>With the data suitably prepared it was time to focus on building up interaction between the new map layers and the soil survey narrative. MapServer is the rendering engine for this project, as it is a well‐established, open source, relatively easy‐to‐use geodata server and it supports a number of open standards and many modern data formats (<ext-link ext-link-type="uri" xlink:href="http://mapserver.gis.umn.edu/">http://mapserver.gis.umn.edu/</ext-link>). Although there are a number of pre‐fabricated or templated interfaces that display MapServer output and allow additional functionalities to be coded in, in order to stay abreast of late developments in user interfaces to geospatial data in the context of a swift Web 2.0, as well as build very specific, unusual functionality (e.g. linking from map data to text), an additional layer of software called ka‐Map! was placed between MapServer and the user (<ext-link ext-link-type="uri" xlink:href="http://ka-map.maptools.org/">http://ka‐map.maptools.org/</ext-link>). ka‐Map! is a young, open source JavaScript application programming interface (API) that tiles and precaches MapServer output and thus gives MapServer output a faster, more agile interface more akin to Google Maps' and Yahoo! Maps', but potentially with more flexibility. For this project, using ka‐Map! promised an improved user experience and opened up additional development avenues.</p><p>Coding by project staff focused on linking the map's soil polygons to the corresponding sections of the narrative in eArchives, the CONTENTdm‐driven module of the Libraries' institutional repository. Rewriting ka‐Map! code to account for this was not an altogether difficult process; with minor alterations to php and JavaScript functions and some interpretation of the URL parameters used internally by the CONTENTdm installation it was possible to send map attributes as search terms to CONTENTdm and let that system behave as though the terms came from its own web forms. Querying or keyword searching of the map data identifies a soil class or classes and presents three links. Clicking the first zooms the map to the given soil. The second link dynamically constructs a URL that calls on the narrative document in eArchives. CONTENTdm constructs an image from the narrative documents using its own word highlighting and scalable resolution and that image gets returned to the map interface for the user to preview. A click on this preview image sends them on to that page of the document in the native eArchives interface. The third link bypasses a preview and goes straight from the map interface to the first search hit of the name of the identified soil class within the narrative document (see <xref ref-type="fig" rid="F_2380260204004">Figure 4</xref>).</p><p>This alpha preview was presented to an <italic>ad hoc</italic> council of soil scientists and agronomists in April 2007. Interface enhancements, on‐map metadata display, and supporting documentation were still underdeveloped at this time, but the reaction from this group was positive and important to the future development of the project. The final section of this paper will discuss this reaction and present a kind of prospectus for work that will extend beyond the November 2007 Agronomy Centennial.</p></sec><sec><title>Future directions</title><p>Contrary to team expectations, the agronomists did not ask for additional map‐to‐narrative functionality or even a link from narrative back to map. Instead, they asked for:<list list-type="bullet"><list-item><label>• </label><p>more map layers, from several eras of soil study and soil mapping technological development and of germane historic interest; and</p></list-item><list-item><label>• </label><p>a greater assisted ability to connect map data with non‐map data.</p></list-item></list>The former was no real surprise. Those familiar with GIS are already aware that one of the great powers of GIS is the ability to pull together geospatial data from different eras and different sectors of the information universe. The second request illustrated the savvy of the agronomists and confirmed a direction for the future of the project that had been discussed by the team but set aside pending more progress on the application itself. In advance of the team's meeting with the soil scientists, as an experiment, an extra layer of map data was included that sat atop the new soil polygon data, which itself was layered over the original map image. Three dots occupied that top level, each of which presented a small popup window when hovered over with a mouse. One dot's popup contained a citation for an article about the soils and vegetation of pre‐Euro settlement in Indiana. The second dot's popup presented several paragraphs of example dummy text. The third popup contained a movie about soil sampling procedures created several years earlier by a Purdue Extension program. The three dots and their accompanying popups had been “floated” into the map on a stream of xml data extracted from a wiki, a Content Management System (CMS), and an external website, respectively. The agronomists' interest in more functionality of this sort speaks to the heart of what <xref ref-type="bibr" rid="b2">Curran <italic>et al.</italic> (2006</xref>, <xref ref-type="bibr" rid="b3">2007</xref>) and indeed countless others have addressed: the capabilities of the more modern, mashable web have altered the expectation and vision of researchers in libraries just as much as it has gaggles of MySpacers and bloggers. These agronomists were seeing that the map, almost as much as any other document or document metaphor online today, could be a platform for content – shared, contributed content – and be content at the same time.</p><p>The agronomists' interest in contributing to the online app verifies sentiments within GIS literature that directly or indirectly address that part of the Public Participation GIS (ppGIS) movement devoted to more implicit, less explicit geospaces (<xref ref-type="bibr" rid="b11">Talen, 2000</xref>; <xref ref-type="bibr" rid="b6">Harris and Weiner, 1998</xref>; <xref ref-type="bibr" rid="b10">Sieber, 2004</xref>; <xref ref-type="bibr" rid="b9">Miller, 2006</xref>). These and other ppGIS scholars have posited that a more democratic GIS would be one that could account for and include conceptions, constructions, and uses of space that are perhaps not explicitly geospatial or are at least not of the Euclidean, mathematical lineage that is the default target of GIS functionality. In some ways, building this soil project with a future in mind that has the map behaving as not only a resurrected library asset, but as a sounding board for different communities of stakeholders; agronomists in Purdue's Agricultural Sciences departments, for sure, but also farmers, historians, and land use analysts, a smattering of ppGIS ideals would be adopted into an environment that also experiences exclusionism and top‐heavy support for GIS, a problem to which Purdue Libraries has begun to pay attention. Maps are rich, almost always interdisciplinary documents, and their important earth science data are complemented by information additionally useful to those with more “secular“ interests in community‐building, local history, archaeology, genealogy, and other campus disciplines. The map mashup model, easily within the ethic of read/write Web 2.0, works just as well, if not especially well, in academic environments where the fount of mashable information is richer (and arguably more esoteric) and includes discipline expertise, other library collections, external datasets, and a potential user population with highly variable experience with traditional GIS work. Much of this is fodder for the models of a ppGIS or GIS/2 that have been written about over the last ten years. The result of the soil survey application – a kind of floating geobibliography – will be another step toward legitimizing the mashup as a viable method for providing access to multiple collections, and, better still, engaging library users.</p><p>So work in the immediate future will focus on preparing at least a 1.0‐level product for the November centennial. The following year should see two forks of work on this project. The more practical of these will be the likely decision to progress to the 1957 Tippecanoe survey, a much more complex document at least in terms of its maps and the geometries therein. Other work, though, will focus on: adding additional explicitly‐spatial content germane to the area and topic; and adding user‐input methods and widgets that will allow agronomists or other interested parties to add literature citations, annotations, or their own map layers. This is in anticipation of a future where more and more library collections will transform into interactable, mashable complements to one another and to the wider world of data and information. The richness of maps as texts, their almost inherent interdisciplinariness, and their potential to become shared space to any number of interested parties, suggests that library users have plenty to say not only about the maps themselves, but to each other; through, across, and over geospace and time.</p></sec><sec><fig position="float" id="F_2380260204001"><label><bold>Figure 1<x> </x></bold></label><caption><p>Close‐up of an obvious member of the black classification</p></caption><graphic xlink:href="2380260204001.tif"></graphic></fig></sec><sec><fig position="float" id="F_2380260204002"><label><bold>Figure 2<x> </x></bold></label><caption><p>Close‐up of the map image before and after segmentation</p></caption><graphic xlink:href="2380260204002.tif"></graphic></fig></sec><sec><fig position="float" id="F_2380260204003"><label><bold>Figure 3<x> </x></bold></label><caption><p>Gaps of unknown soil type (areas in white) left by removal of black classes</p></caption><graphic 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Designmethodologyapproach The components of a 1906 soil survey document were scanned and their contents were extracted using several different methods, chief among them imagery segmentation and classification. The resulting datasets included a fulltext version of the original narrative and two georeferenced versions of the soil survey map. Findings Going several steps beyond just scanning, including the application of geographic information system GIS capabilities, adds significant value to geospatial materials whose contents are still relevant but whose formats are cumbersome. In addition, this allows for a GIS platform to which other maps and content can be added. Originalityvalue This is a unique approach to enhancing content through GIS.</abstract><subject><genre>Keywords</genre><topic>Geographic information systems</topic><topic>Soil surveys</topic><topic>Agronomy</topic></subject><relatedItem type="host"><titleInfo><title>Library Hi Tech</title></titleInfo><genre type="Journal">journal</genre><subject><genre>Emerald Subject Group</genre><topic authority="SubjectCodesPrimary" authorityURI="cat-IKM">Information & knowledge management</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-ICT">Information & communications technology</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-INT">Internet</topic></subject><subject><genre>Emerald Subject Group</genre><topic authority="SubjectCodesPrimary" authorityURI="cat-LISC">Library & information science</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-IBRT">Information behaviour & retrieval</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-LLM">Librarianship/library management</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-IUS">Information user studies</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-MTD">Metadata</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-LTC">Library technology</topic></subject><identifier type="ISSN">0737-8831</identifier><identifier type="PublisherID">lht</identifier><identifier type="DOI">10.1108/lht</identifier><part><date>2008</date><detail type="volume"><caption>vol.</caption><number>26</number></detail><detail type="issue"><caption>no.</caption><number>2</number></detail><extent unit="pages"><start>201</start><end>212</end></extent></part></relatedItem><identifier type="istex">02D7534BF610E0F313A130657F1D7898C1843106</identifier><identifier type="DOI">10.1108/07378830810880315</identifier><identifier type="filenameID">2380260204</identifier><identifier type="original-pdf">2380260204.pdf</identifier><identifier type="href">07378830810880315.pdf</identifier><accessCondition type="use and reproduction" contentType="copyright">© Emerald Group Publishing Limited</accessCondition><recordInfo><recordContentSource>EMERALD</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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