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380 Cognitive Mapping and GIS for Community-Based Resource Identication Figure 6. Education resources: Cognitive mapping vs. resource guides Figure 7. Health care resources: Cognitive mapping vs. resource guides 381 Cognitive Mapping and GIS for Community-Based Resource Identication County resource guides failed to provide adequate information. Except in education and childcare, the resource guides fell far short of the number of resources identied by the mapping participants. While there are many childcare resources in the Jefferson County information guides, there is little overlap between the childcare listed in the Jeffer- son County guides and the childcare identied by the cognitive mapping. The GIS maps effectively demonstrate such knowledge gaps. Second, there are a signicant number of resources in Denver County (east of Jefferson County) that providers and clients identify. Rea- sonable accessibility to Denver County, as well as lack of availability of the resources in Jefferson County, likely accounts for this trend. Building a community-based SOC will require Jefferson County to nd ways to offer some of these services locally, a challenge that will require developing community partnerships to overcome the nancial constraints which the County faces. Third, opposite of the previous trend, Jefferson County resource guides provide mainly Denver locations for some types of resources, even though the same resources exist in numerous places in Jefferson County. Available resources closer to Jefferson County residents are a fundamental component of SOC and, in this trend, require only disseminating the information effectively, which is a low-cost method to improve community-based service delivery. Finally, there is a large disparity in knowledge between clients and providers. With the exception of 3 of the 24 categories, Education, Recreation, and Commercial Resources, the providers and clients did not overlap signicantly in knowledge about resources. Providers know more about traditional resources such as other agencies or governmentally-supported social services, while clients know about resources of a less traditional nature, such as churches, motels, and parks where teenagers gathered to socialize and engage in recreational sports activities. Although these informal resources are not referral services that providers typically pass along to clients, they are important community-based resources to share with clients. In creating a community-based SOC, providers need to be aware of the alternative methods clients use to meet their needs. In some instances, this new information will lead to the creation of government/community partnerships to more effectively and efciently deliver services. In other circumstances, the additional knowledge of resources will provide clients with options and/ or ll gaps in needs that traditional government and community providers cannot meet. Lessons LeArned Several problems directly and indirectly related to the GIS component of the project became apparent and required adjustments to the pro- cedures or accommodations to the expected output. These include research procedures that are incompatible with social service agencies’ capacity, issues of client condentiality, repeat rates, incomplete and/or inaccurate databases for coding resource locations, coding protocols, and mapping accuracy. First, as has been found before, many county and local agencies lack leadership that under- stands the value of GIS in policy decision-making (Greene, 2000; Nedovic-Budic, 1996; Ventura, 1995; Worrall & Bond, 1997). Hence, many agen- cies lack the technical ability to employ GIS and, consequently, also lack the understanding to work effectively and efciently with the researchers. Furthermore, because social service agencies typically do not have a GIS analyst on staff, data and map les have limited usefulness beyond the initial analysis as presented in the nal report. Finally, human service agencies have organiza- tional procedures that create signicant barriers in implementing research projects, barriers that need to be addressed in the project planning stages (Ventura, 1995). Jefferson County Human Services suffered from all three impediments 382 Cognitive Mapping and GIS for Community-Based Resource Identication and was exacerbated by the high turnover of the staff. In the rst year, two-thirds of the project staff left. By the middle of the second year, only one person out of nine key project staff remained. Those who left included the project manager and the principal investigator, both of who had been replaced twice. Within 18 months, none of the people who conceptualized and wrote the HHS grant were involved in the project. Institutional memory was wiped clean and new staff was unfamiliar and wary of many components laid out in the grant proposal, including the untra- ditional resource identication method. Higher administrative support for the innovative project waned, and “business as usual” reasserted itself as the dominant paradigm. It became clear that the resource database developed through the map- ping process would not be updated on a regular basis and, perhaps, not disseminated throughout the organization if left to Jefferson County. The CIPP sought out a more stable organization to house the resource data, Colorado 2-1-1, with the permission of the rst project manager. Second, human service agencies as well as educational institutions cannot share client/stu- dent data. This presents a signicant research barrier when the project requires participation of these populations. Ideally, individuals within the organizations would have both the access to the data and sophistication to manipulate the data in accordance with standard research protocols. This is unlikely to be the case in institutions which are nancially strapped and lack the vision or political will to invest in trained personnel and needed research tools. To ameliorate these condi- tions, project planning must include agreed-upon protocols for effectively and efciently handling condential data. Third, unique to this project was the cre- ation of a “repeat rate” to set a standard for data density. The 80% repeat rate was selected for efciency of resources, based on an extrapola- tion of the average number of points per map and time needed to code and enter the data for each map. Unknown was how many participants/ maps were needed to reach the 80% repeat rate in each of the 24 categories. Initially, the CIPP recommended target was 450 participants. This number was revised downward by Jefferson County Human Services to a maximum of 250 participants. From the 247 actual participants, the 80% repeat rate was reached in only two of the 24 resource categories. The average repeat rate was 55% across all categories, indicating that more than 250 participants were needed to reach 80%. Whether 450 participants were ultimately required is unknown. More importantly, did the lower repeat rate signicantly affect the quality of the project? Certainly, fewer resources were identied at the 55% rate; but 1,480 resources not in Jefferson County resource guides were identi- ed; not an insignicant contribution to building a more comprehensive social services. Fourth, in the process of coding the maps and sorting the data to nd repeated addresses or groupings by type of provider, and so forth, it was discovered that precise alphanumeric coding was critical. With the large number of data elds (attributes) assigned to each participant, there were inconsistencies in some of the categories. The data cleaning was more extensive than anticipated. Future projects should utilize numeric coding in attributes to the fullest extent possible and develop strict alphanumeric standards for addresses, or- ganizational names, and other alpha elds. Finally, to nd resource addresses, MapQuest and the Denver metro area phone book were used. MapQuest was the most efcient method but had the most errors, as discovered when the address was imported into ArcMap. A cross-check with the phone books corrected most of these errors. Nine percent of the mapping points were unidentiable due to a combination of missing information in MapQuest and the phone book, and poor location information on the hand drawn maps. The latter accounted for a greater proportion of the unidentied points, especially resources such as neighborhood parks and unnamed resources 383 Cognitive Mapping and GIS for Community-Based Resource Identication such as “soup kitchen.” Rather than rely solely on participants naming the nearest cross streets to such resources, the closest known commercial entity should be identied. This redundancy will reduce missing data due to participant error in naming streets. f uture t rends While this project was limited to identifying resources, spatial patterns of resource locations, and knowledge gaps, the collected data can be mined further. More specic uses can be created, such as a searchable Web-based provider resource database and the identication of physical and/or service areas with inadequate resources in relation to socio-economic deprivation areas. The latter allows providers to demonstrate specic needs, important for several reasons, including the pursuit of future programmatic funding. These specic uses are described in greater detail as follows: • Provider resource database: In the future, the Web-based database can be converted into a tool for social service providers to identify available resources and the most accessible locations for clients (Worrall & Bond, 1997). The end user (a case-worker) would be able to search for particular re- sources based on any number of criteria or a combination of criteria. For example, one might enter necessary criteria such as Rental Assistance Housing Resource located within three miles of a given location that also caters to Spanish-speaking clientele. After these attributes or criteria are entered into the appropriate locations on the Webpage, a list of all the resources or providers that t the criteria could be retrieved, similar to the business name search feature available through a site such as MapQuest. Finally, digital maps could be generated with driving directions for the case-worker to print out for the client. It is also possible to map the public transportation routes to services. • Needs assessments: The database can be used to conduct comprehensive, quanti- able, and defensible needs assessments. A social service provider administrator or grant writer could search the data described above in conjunction with Census data and the County’s client locations to reveal areas of need or areas of excess (Bond & Devine, 1991; Worrall & Bond, 1997). 6 A strategic plan could be developed to determine where a new ofce or access point for a particular resource should be located to serve the great- est number of clients. This type of spatial analysis based on quantiable numbers and distances can be used to justify a particular course of action either for internal/external accountability or to acquire funding for vari- ous projects aimed at community resource and social service distribution. Acknow Ledg Ments The author would like to thank April Smith, Department of Psychology, Colorado State Uni- versity, and Mary Tye, Department of Psychol- ogy, Colorado State University, for running the workshops and coding the data; David Wallick, Colorado Institute of Public Policy, Colorado State University, for conducting the GIS analysis; and Juliana Hissrich for providing administrative support to the project. conc Lus Ion Cognitive mapping combined with GIS analysis is a powerful method for identifying community resources by providing: (1) a comprehensive database of existing services; (2) a basis to build communication networks and cooperation among government and community providers; (3) the 384 Cognitive Mapping and GIS for Community-Based Resource Identication ability to create an efcient system that avoids duplication of efforts; (4) an understanding of the geographical distribution of resources; (5) the identication of resources lacking in the county and specic communities; and (6) knowledge differences among diverse participant groups. The addition of 1,480 resource locations within the seven study areas (only a portion of Jefferson County) nearly tripled the number of resources and services listed in the Jefferson County guides. Ultimately, service delivery in SOC is about building partnerships across the multiple services and bringing in new, even sometimes untradi- tional, community partners. Family involvement is the key in this collaborative arrangement. Similar to untraditional community partners and resources, families as partners do not t easily within current social service delivery structures, values, and beliefs. Recognizing, valuing, and partnering with resource providers identied by clients and community members is one important step toward shifting practices. Cognitive map- ping with GIS provides a tool for taking the rst critical steps. r eferences Bond, D., & Devine, P. (1991). The role of geo- graphic information systems in survey analysis. The Statistician, 40, 209-215. Daniels, K., & Johnson, G. 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Jour- nal of Mathematical Psychology, 35, 371-393. 386 Cognitive Mapping and GIS for Community-Based Resource Identication endnotes 1 The project was supported by grant #90CA1715/01, CFDA #93.570 from the Federal Department of Health and Human Services through Jefferson County, Colo- rado. 2 The term cognitive mapping is used for a va- riety of techniques, including “fuzzy cogni- tive mapping,” a technique that builds mental maps of perceptions from focus-group and interviews (Hjortso, Christensen, & Tarp, 2005; Hobbs et al., 2002). In this project, cognitive mapping means hand-drawn maps of tangible community resources and locations, a geographical data collection technique new to GIS. 3 Nine percent of the mapping points could not be accurately located and were dropped from the analysis. Of the remaining 89%, two possible location errors could occur in transferring the cognitive map information into a database for ArcMap. First, multiple coders could use different alphanumeric codes, thereby making the same resource appear as a different resource. To correct this error, the data was cleaned by conduct- ing sorts on multiple columns in the excel spreadsheet to reveal unknown duplicates. For example, a search on “Research Name” might nd the same resource with inconsis- tent address codes. If the address did not match exactly (e.g., one was coded with “St.” and another coded with “Street,” the coding was corrected to be consistent. Simi- lar searches were done on other categories such as street address, street name, and zip code. The data was cleaned accordingly. The second error was from incorrect addresses in the MapQuest and/or Dex directory. The Dex directory is the ofcial metropolitan phone and address directory and should have a high level of reliability; however, the actual reliability rate is unknown. To correct for possible errors, all identied social services not in the Jefferson County resource guides (e.g., soup kitchens, English as a Second Language courses, support groups, etc.) were called to verify the address. It was assumed that the Jefferson County resource guides had accurate information. 4 All identied resources were provided to Colorado’s 2-1-1 system, which is the national abbreviated dialing code for free access to health and human services infor- mation and referral (I&R). 2-1-1 is an easy- to-remember and universally-recognizable number that makes a critical connection between individuals and families in need and the appropriate community-based organiza- tions and government agencies. Housing the data with 2-1-1 allows statewide access to resources and bi-annual updating to keep the information current. Colorado 2-1-1 system is the depository for the resources collected in this project. Web searchable database of resources can be found at http://211colorado. org/ 5 CIPP provided Jefferson County with the ethnic enclave areas based on the 2000 Census. The Asian communities fell out- side the project boundaries set by Jefferson County (see Figure 1) and, unlike Russians, Latinos, and Native Americans, Jefferson County did not request mapping with the Asian community. 6 For example, it might be found that 65% of all users of a certain type of resource (this data would be collected by cognitive map- ping alone) live “x” number of miles away (analysis performed by the GIS system) from a particular needed or frequently-accessed resource (gathered through cognitive map- ping and other sources). 387 Cognitive Mapping and GIS for Community-Based Resource Identication Append IX Only forty percent of the participants provided demographic information, which limits the ability to determine the gender, age, and ethnic- ity/race of the participants. However, there is no way to determine the representativeness of the sample on these traditional demographics since the population characteristics are unknown. Table 2. Demographics of participants (n=100) Demographic characteristic All participants (n=100) Providers (n=19) Clients (n=72) Community Residents (n=9) Number and percent female 85% 90% 82% 100% Average age 34.39 39.75 31.86 44.83 Number and percent Caucasian 62% 68% 64% 33% Number and percent Latino 19% 5% 24% 11% Number and percent African American 6% 0% 4% 33% Number and percent Native American 9% 21% 4% 22% Number and percent Other 4% 5% 3% 0% Even among the clients, the demographics are not available because most of the client records were incomplete. Unlike many social research projects, demographic representation is less of a concern. For the identication of resources, a cross-section of the types of people who use or provide services and the geographical distribution of their knowledge was most important, of which both criteria were met. This work was previously published in Emerging Spatial Information Systems and Applications, edited by B. Hilton, pp. 326- 350, copyright 2007 by IGI Publishing (an imprint of IGI Global). 388 Chapter XLV Collaborative Mapping and GIS: An Alternative Geographic Information Framework Edward Mac Gillavry Webmapper, The Netherlands Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Abstr Act The collection and dissemination of geographic information has long been the prerogative of national mapping agencies. Nowadays, location-aware mobile devices could potentially turn everyone into a mapmaker. Collaborative mapping is an initiative to collectively produce models of real-world loca- tions online that people can then access and use to virtually annotate locations in space. This chapter describes the technical and social developments that underpin this revolution in mapmaking. It presents a framework for an alternative geographic information infrastructure that draws from collaborative mapping initiatives and builds on established Web technologies. Storing geographic information in machine-readable formats and exchanging geographic information through Web services, collaborative mapping may enable the “napsterisation” of geographic information, thus providing complementary and alternative geographic information from the products created by national mapping agencies. Introduct Ion Since the Enlightenment, mapping and the pro- duction of geographic information have been institutionalised: the map is the power. At home, maps were used as an instrument for nation building as nation states emerged: a legitimi- sation device (McHafe, 1995). People learned about their country and administrations needed a tool to govern the territory. Away from home, maps were an instrument for colonisation, when Africa and Asia were split among the European nation-states. During the last few decades, there has been rapid democratisation of geographic information and maps. Sawicki and Craig (1996) distinguish 389 Collaborative Mapping and GIS three ways in which this movement is apparent. First, the locus of computing power and data access is broadening. Second, the level of skills to turn raw geospatial data into geographic information has become less demanding. Third, the locus of applications has moved closer to the citizenry. Geographic information systems moved from mainframes and the UNIX operating system onto personal computers and the Windows operating system. From research and government, GIS spread into the business sector. The PARC Xerox Map Server and Virtual Tourist brought maps to everyone’s PC in the late 1990s, followed by online map Web sites such as MapQuest and Multimap. In 1997, Brandon Plewe noted that “the Internet holds promise for exponential increases in the efciency and effectiveness of the ways in which we obtain, use and share geographic information in all its forms” (Plewe, 1997). In July 2002, 7.1 million European users visited one of the many online map Web sites (Nielsen//NetRatings, 2002). Google Maps, introduced in February 2005, reached almost 1.7 million visitors in that month (Buchwalter, 2005). Although maps are more widely used than ever, the production of geographic information, and especially mapping, is still highly concentrated among national mapping agencies and the GI in- dustry. But this oligarchy is soon to be dissolved, for we see the third aspect of the democratisation of geographic information–the locus of applica- tions moving closer to the citizenry–becomes apparent now that location-aware mobile devices are coming within everyone’s reach. GPS units are not only available to surveyors anymore, as cheaper devices are sold for outdoor recreation. Also, small GPS antennae can communicate with other devices over Bluetooth, and there are already mobile phones and personal digital assistants (PDAs) for the consumer market that have GPS-chips built in. At the same time, digital maps have become portable. Various mobile phone operators have started to deliver location-based services to mobile devices. Mobile phones come with route planning applications, thus making in-car navigation sys- tems redundant. Maps are not only delivered to the desktop, but also to mobile phones and PDAs, requiring new visualisations as the screen size, resolution, and use patterns differ signicantly. Collaborative mapping is an initiative to col- lectively create models of real-world locations online that anyone can access and use to virtually annotate locations in space (McClellan, 2003). The value of the annotations is determined by physi- cal and social proximity, the former expressed in distance, the latter in “degrees of separation.” Thus, the informational value and the pertinence of spatial annotations is not only dependent on physical distance, but also dependent on the trust relationship between individuals or groups of people through social networks: the “Web of Trust” (Espinoza, Persson, Sandin, Nystrom, Cacciatore, & Bylund, 2001). However, there is a discrepancy between physi- cal and social proximity. Privacy and personal freedom become highly important issues when one’s location is related to their social behaviour. On the other hand, the fear of surveillance that accompanies positioning is already gradually reducing in society (Ahas & Mark, 2005). Fur- thermore, this discrepancy can be mediated by users themselves by storing annotations and tracks locally, thus creating distributed repositories, and by explicitly setting the level of privacy on each of these annotations and tracks. Finally, users themselves remain in control of their so- cial identication–their preferences and social network–while they make use of collaborative mapping services, whereas, for example, the so- cial positioning method aggregates these social characteristics to study the space-time behaviour of society (Ahas & Mark, 2005). Collaborative mapping services are therefore less pervasive in the privacy of their users because users negotiate the trade-off between the benets of the service and their privacy concerns. [...]... study of being In the domain of information systems and AI, ontology has a somewhat different connotation as an “explicit specification of a conceptualization” (Gruber, 199 3; Farquhar, Fikes, & Rice, 199 6) and provides a more pragmatic definition: Ontologies are explicit specifications of domain conceptualization names and describe the entities that may exist in that domain and relationships among those... different changes of land use category Depending on the land use category, the planning authority classifies a certain number of zones Zones are spatial as well as administrative features managed by an environmental agency in accordance with the environmental regulations The definition of the organization (in this case-environment and conservation-and-wildlife-organizations) relevant to zonal planning... subsumptions In addition, axioms could be added to make implicit subsumptions explicit Ontology Repository The ontology repository consists of i) core ontology, ii) domain ontology, iii) user ontology, and iv) Web procedure/services ontology In general, these repositories contain a set of axioms (e.g., assertion of class subsumptions/equivalence), and taxonomic class hierarchies All of these ontologies... characterize the semantic content of spatial data and model The representation of “river” or “body of water” depends on the context of the requesting agent Depending on the context, the geometric representations of “river” and “body of water” could be also different; the resulting intersection of a “river” and a “highway” could be a point feature in one representation while it could be a polygon feature in another... generator have bben developed The ontology expresses the widely used specification of a “non-point pollution model” or what is commonly known as “simple method” [41] for estimating surface runoff or pollutant load The first step in markup of the domain components is to describe each constituent component of the pollution model It consists of a declarative description of the model’s properties The objective... problems such as inconsistency between ad-hoc ontologies which might be built into the system (Fonseca & Egenhofer, 199 9) This approach to ontology in geo-spatial service management would address issues concerning knowledge sharing by creating components from ontology in an object-oriented fashion, using classical object-oriented concepts such as multiple inheritances Thus, spatial ontology should allow... (Smith, 199 6) The study of parts and boundaries of the whole is an important aspect of representing the multiple constituents of a complex spatial object in relation to adjacency, containment, selection, or the separateness of constituent parts Ontology in this sense needs also to be viewed as a mediator for knowledge exchange, to build a business application that can enable data integration and avoid... name of a location is established through consensus among a group of people It is not determined a priori, but evolves 396 as people in a community add new annotations in response to the needs of the moment For example, in the GeoNotes service (Espinoza et al., 2001), a “location tag” determines the exact location of an annotation in the service in case the accuracy of the positioning technology of either... model schema of legacy models and native data structure to model manager and help generate meta-model catalog An ontology agent provides standardized descriptions of model constructs in conformity with ontological specifications Its responsibility also includes ontology deployment, validation of specification leading to creation of new wrapper objects in the database, and updating the ontology repository... objects that belong to different databases and the resolution of their semantic differences (Kashyap & Sheth, 199 6) The use of an ontology (Guarino & Giaretta, 199 5) as a framework for defining similarity among objects has the benefit of a formal definition for concepts in different metadata, a definition that could be used to define axioms for semantic translation between ontologies The term “ontology” . project was the cre- ation of a “repeat rate” to set a standard for data density. The 80% repeat rate was selected for efciency of resources, based on an extrapola- tion of the average number of. permission of IGI Global is prohibited. Abstr Act The collection and dissemination of geographic information has long been the prerogative of national mapping agencies. Nowadays, location-aware mobile. form of community-based participatory planning (Harris et al., 199 6) or as a means to obtain data for the social positioning method as input for urban planning (Ahas & Mark, 2005), one has
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