Handbook of Research on Geoinformatics - Hassan A. Karimi Part 9 potx

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Figure 6. Education resources: Cognitive mapping vs. resource guides

Figure 7. Health care resources: Cognitive mapping vs. resource guides

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County resource guides failed to provide adequate

information. Except in education and childcare,

the resource guides fell far short of the number of

resources identified 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 identified by

the cognitive mapping. The GIS maps effectively

demonstrate such knowledge gaps.

Second, there are a significant 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 find ways to offer some of these services

locally, a challenge that will require developing

community partnerships to overcome the financial

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 significantly 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 efficiently deliver services.

In other circumstances, the additional knowledge

of resources will provide clients with options and/

or fill 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 confidentiality, 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 efficiently with the researchers.

Furthermore, because social service agencies

typically do not have a GIS analyst on staff, data

and map files have limited usefulness beyond the

initial analysis as presented in the final report.

Finally, human service agencies have organiza￾tional procedures that create significant 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

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and was exacerbated by the high turnover of the

staff. In the first 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 identification 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 first project manager.

Second, human service agencies as well as

educational institutions cannot share client/stu￾dent data. This presents a significant 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 financially 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 efficiently handling

confidential 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

efficiency 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 significantly affect the quality

of the project? Certainly, fewer resources were

identified at the 55% rate; but 1,480 resources not

in Jefferson County resource guides were identi￾fied; not an insignificant contribution to building

a more comprehensive social services.

Fourth, in the process of coding the maps

and sorting the data to find 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 fields

(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 fields.

Finally, to find resource addresses, MapQuest

and the Denver metro area phone book were used.

MapQuest was the most efficient 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

unidentifiable 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 unidentified points, especially resources such

as neighborhood parks and unnamed resources

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Cognitive Mapping and GIS for Community-Based Resource Identification

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 identified. 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 specific uses can be created,

such as a searchable Web-based provider resource

database and the identification of physical and/or

service areas with inadequate resources in relation

to socio-economic deprivation areas. The latter

allows providers to demonstrate specific needs,

important for several reasons, including the pursuit

of future programmatic funding. These specific

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

fit 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, quantifi￾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 office 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 quantifiable 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.

C onc 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

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Cognitive Mapping and GIS for Community-Based Resource Identification

ability to create an efficient system that avoids

duplication of efforts; (4) an understanding of

the geographical distribution of resources; (5) the

identification of resources lacking in the county

and specific 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 fit easily

within current social service delivery structures,

values, and beliefs. Recognizing, valuing, and

partnering with resource providers identified by

clients and community members is one important

step toward shifting practices. Cognitive map￾ping with GIS provides a tool for taking the first

critical steps.

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