Three models of constructing GIS tools in the non-profit sector
Chandra Rice Independent Accreditation Services Corporation 23 Morden St. Hamilton Ontario L8R 1P6 Canada Abstract Not only are local governments implementing their own geospatial IT solutions, but they are also being asked to engage in solutions on a different frontier: the nonprofit sector. Our paper presents the challenges and solutions specific to sharing spatial data with and within the nonprofit sector. We have been working with nonprofit collaboratives in three Ontario regions to build online information sharing systems or data warehouses, each with a significant GIS component, and each in collaboration with the municipal government. We tested a different model in each region. In Hamilton, we built the warehouse using a stand-alone expert model, drawing on the expertise a small independent team, with the expectation that once built, the community will use it. In York, we built the warehouse using the committee strengthening model, facilitating the collaboration of a powerful regional committee who will shape and use the warehouse. In Peel, we built the warehouse using the community-building model, working to draw the community together as a whole. A comparison of the three models has provided valuable insight into the strategies and tools needed to create collaborative geospatial IT solutions, adding value to the work of both the nonprofits and the municipal government. Introduction Not only are local government implementing their own geospatial IT solutions, but they are also being asked to engage in solutions on a different frontier: the nonprofit sector. Our paper presents the challenges and solutions specific to sharing spatial data with and within the nonprofit sector. We have been working with nonprofit collaboratives in three Ontario regions (York, Peel, and Hamilton) to build online information sharing systems or data warehouses, each with a significant GIS component, and each in collaboration with the municipal government. We tested a different model in each region, and a comparison of the three models has provided valuable insight into the strategies and tools needed to create collaborative geospatial IT solutions, adding value to the work of both the nonprofits and the municipal government. This paper outlines each of the three models, discussing
Before describing the three models of building a data warehouse, the paper outlines
The end goal for many levels of government and for most of the nonprofit sector is to create healthy and sustainable communities. But how are we to accomplish this goal? There is a chain of ideas that bring us to the use of a data warehouse. This chain is described below. To create healthy and sustainable communities we need to build the community’s capacity to solve increasingly complex problems. For example, the factors that increase complexity are population growth, increased diversity, and increased vulnerability among some populations. The social risks or problems that stem from these factors cross organizational boundaries, making it difficult to solve problems as individual organizations. Thus addressing these factors is increasingly a matter of collaborative work between nonprofit groups, community associations, and local government. Collaboration between these groups enables the coordination between specialized and generalized services that cross traditional organizational boundaries and helps neutralize the factors at play. To have successful collaboration, organizations need to be able to share their data and analysis. Sharing data helps collaboratives build a more comprehensive picture of the community than any one organization can do individually. Using this data to collaboratively analyze the problems facing the community builds community capacity, strengthening its health and sustainability. When we did the research to find the best means of sharing data and analysis at a level that would be useful to nonprofit organizations working in the community, data warehouses proved to be the best technology available. Basic overview of the concept of a data warehouse A data warehouse is a large repository of information that is stored in a virtual site. Because the site is virtual (in a computer, or series of computers, linked to the internet) a data warehouse allows anyone who has permission to access all of the information in the database from wherever they wish. The type of data contained in a data warehouse depends on the purpose for it. There are many excellent examples of data warehouses that have been built all over North America. Each one of the data warehouses takes a different approach to the building process. What is unique about our project is that we have the taken the opportunity to consciously try three different approaches or models to the process of building approximately the same type of data warehouse. Such opportunity allows us to draw interesting conclusions about features, conditions, and pitfalls that arise out of each model. Before we discuss each model, we need to describe the end product that each model is working toward. The end product that we are building is best described by examining four aspects of the data warehouses that we are building:
At its core, each data warehouse will have census data, health statistics, crime statistics, and statistics from the public school board. This data will all be presented at the enumeration (or dissemination) area level. The same standards that are used by Statistics Canada (e.g. the joining enumeration areas where there is a cell count of less than five, or and random rounding to the nearest five) will be used to protect the confidentiality of the data. The other types of data that are included in the database will depend somewhat on the organizations that wish to partake in the project in each region. Each organization that participates is asked to decide what data they will contribute to each of the levels of the data warehouse. As we will see described a little later, the organization can be very specific about with whom they share certain parts of their data. The types of data that are contributed by the organizations range from very local data, for example, on church participation rates, and little league sports activities through to regional data on mental health, sexual and physical abuse, literacy, poverty, cultural diversity, homelessness, etc. The organization is asked to contribute the data in a common unit of either postal code or enumeration area so that the data is comparable across different communities. Types of Interface There are three ways that the data is provided to the user of the data warehouse:
Levels of Access One of the essential features to all three data warehouse building models is the various levels of access. At its most simple, the data warehouse will have three different levels of access (the actual permutations of the levels are discussed in more detail in the description of the models):
The interactive level provides a computing space where organizations who have contributed data can share directly with each other. Those with security clearance are able to perform analyses on the primary data, and combine this with both their own data and the data that is publicly available. Access to this area is by login and password on an individual or organizational basis. A unique secure area is provided to each organization for maintaining its own database. This secure level is accessible only to those whose login provides them with permission. Inside this secure area, organizations store and analyze as much data as they wish. As an example, information at this level may include case files. These organizations decide how much information is available to those at the interactive level, and how much will be available at the public level. They also decide on the form of these data. Groupings of Community The difficulty traditionally with gathering and maintaining neighbourhood data is that each individual or organization defines the neighbourhood differently. A ‘neighbourhood’ is a socially constructed area. People in an area will identify different neighbourhoods for different purposes. Yet despite the difficulties defining neighbourhood boundaries, we know that many things compel us to work towards gathering and analyzing data at the neighbourhood level. For example, having data available at the neighbourhood level increases the capacity of local groups to partner with other groups in other parts of a city or regional area to develop larger initiatives. US research shows that having data solely at the city level misses many local differences and pockets of strength and/or distress. Healthy cities are based on healthy neighbourhoods. Policy decision-making is enhanced by the capacity to evaluate outcomes at the neighbourhood level, which can also enable the targeting of resources at the neighbourhood level so that local needs can be met more effectively. Strategic planning for social development is most successful if done at the neighbourhood level. Collaborative strategies at the neighbourhood level need data organized at that level. Our data warehouse allows the user to choose amongst a variety of different neighbourhood boundaries in the presentation of the data. They are also able to create their own neighbourhoods based either on enumeration areas (for census data) or on postal codes (for most other data). Now that we have a sense of what is being attempted in the three regions, let us turn to the models of how we are building datawarehouses in three different regions. We will move from the smallest and most contained model through to the largest and most open model. Model One: Stand-Alone Expert Description of the Model The model of the stand-alone expert uses a small team of experts to build the data warehouse. The small team, using their previously gained knowledge and expertise to guide them, completes all decision-making. The larger community will be invited to use and participate in the data warehouse once it is completed. Background of the Project using this Model Located in Hamilton, this project was initiated by a two-person partnership between a professor at McMaster University and a community developer/GIS expert from the Social Planning and Research Council of Hamilton. The two people brought sufficient expertise to the table to design and manage the project, and were able hire any technical expertise they needed to implement the project. The partners have a deep connection with various aspects of the region, sitting on many different committees, both governmental and in the nonprofit sector. Specifically, their connections to the municipal government are soft, friendly relations. The municipality is badly strapped for resources, and welcomes any work on the part of the non-profit community. The two partners are taking an “if-you-build-it-they-will-come” approach. They have done a series of presentations to the key players in the community such as the police, school board and Neighbourhood Watch to ensure that the community is aware of what they are building and what it does, so that other projects in the region can be dovetailed with it. The Differences between this Project and the Full Data Warehouse At present the project only has funding for the first phase of development. The first phase has the three levels of access, but no variations. It has a variety of definitions of neighbourhood, but no ability to create one’s own neighbourhood. It has a fully functioning statistics and social indicators interface, but the map interface is limited to static pre-created maps. The two partners are currently seeking ongoing funding to complete phases two and three. Conditions that make this model possible:
This model is the simplest to manage. The decisions are made on the basis of expertise. The picture created is narrow, specific and well founded. It requires considerably fewer resources. The choices in framework and narrative are straightforward and consistent. There is no confusion about the project in the community. There are no unfounded expectations created. The Cons We do not know if the community will use it. Despite the soft connections into the community, and the presentations to various potential partners, there is not a pre-established group of stakeholders to use it and encourage others to use it as well. Also there is no residual community capacity built into the project. There are no new connections made. Model two: Committee Strengthener Description of the Model The model of the committee strengthener uses a medium sized (20-25 people) committee of community leaders to build the data warehouse. All decision-making is completed by the committee using consensus-building activities and, occasionally, outside experts. The committee represents enough of the community for the immediate future of the project to be assured. The larger community will be drawn into the project towards the end of the building process. Background of the Project using this Model Located in York, the project was spearheaded by the York Region Advisory Forum on Children and Families. YRAF was looking for a way to do better planning and decided that building a data warehouse was the way. The people who sit at the table are the ‘big players’ in the region, and the participation from the regional government is both explicit and fairly trusting. It remains a fairly closed group. They have been required by the primary funding body (The Ontario Trillium Foundation) to add a partner from the hospital and from the police force up front – during the design process. Beyond this requirement, they see this tool as beginning with their use, and moving slowly towards use by the general public. There is very little discussion at this point around how to bring other players to the table; discussions center around how to make the existing players at the table comfortable with the trust issues around the data. They are an important enough group in the region that they do not need to have soft contacts with the community (although they do) because they know that their lead will be followed. The Differences At present the project has funding for phase one and two of development. By the end of the project, York will have the three basic levels of access, but with many variations within the interactive level depending on the relationship between each of the committee members. It has a variety of definitions of neighbourhood, and the capacity to create one’s own neighbourhoods, but the public will not be able to save their neighbourhoods when they log off the site, whereas committee members will be able to save any number of self-defined neighbourhoods. It will have the fully functioning statistics and social indicators interface, and the maps interface will also be included and fully functioning. YRAF is not currently seeking funding, but is interested in looking at gaining more funding in the future to put in place some of the extras for which the third region has funding. Conditions that make this model possible:
The trust levels in a healthy committee allow for almost instant access to the most interesting types of community level data. The decisions are well and broadly considered. The capacity of the committee is deeply strengthened, not only in terms of the actual tool, but also in terms of their ability to work together collaboratively. The success of the data warehouse is completely owned by the committee members. Some new connections are made, and other pre-existing partnerships are solidified. The Cons The boundaries between the committee and the remainder of the community are amplified. The decision-making by the committee is done with the best interests of the committee in mind, which may diminish the appeal to the larger community. Although it strengthens the capacity of the committee it does not necessarily strengthen the capacity of the wider community. Model three: Community Builder Description of the Model The model of the community builder uses a broad approach to engaging the whole community to build the data warehouse. All decision-making is completed by a design group composed of representatives from collaboratives and organizations from across the region with regular wider consensus building workshops held with the community at large to review decisions made by the design group. The design group, represents in a sense, the community; representatives from each collaborative or organization are constantly asked to engage their community’s participation in the process, so there is not a later attempt at getting larger participation. Background of the Project using this Model Located in Peel, the project was initiated by the Social Planning Council of Peel. Working in the Peel region to encourage citizen participation in planning and policies around social services, SPC Peel felt that the process of building a data warehouse would help build community capacity. SPC Peel sees itself as the facilitator of a larger community process. SPC Peel has the social capital or trust from the community by virtue of its role as a supporter and resource to many initiatives toward social change and service provision in the Peel Community. As part of establishing the community support for building a data warehouse, SPC Peel completed a full feasibility study that involved community consultations with a broad range of community stakeholders. The concerns raised in the feasibility study were used to shape the picture of what the data warehouse would look like in all three communities. The Differences The project has funding for not only all three phases, but also for several other features. The project will work to involve technologies such as hand held computers and cameras to record a variety of new types of data. They also have funding for a fairly strong outreach program to include organizations in the process that need assistance getting their data ready to be included in the data warehouse. SPC Peel is seeking funding for translating the warehouse into the various languages of the region, and to strengthen its sustainability after the four years of funding. Conditions that make this model possible:
This version of building the data warehouse engages the widest range of organizations and individuals. It fosters and builds the community’s capacity. It strengthens existing collaboratives in the region and builds many new connections. The decisions are made with the widest audience’s participation. The community as a whole owns the success of the data warehouse. The Cons It can be very difficult to avoid confusion about the process and the outcomes. Coordinating meetings and decision-making workshops is time consuming and the process can easily stall on a seemingly simple decision. The lack of narrow focus can make the presentation of the data difficult. Conclusion The process of trying three different models of building a data warehouse has led us to better understand the relationship between the choice of model and the outcomes within the community. Some communities are best suited both in terms of resources and desired outcomes to a stand-alone expert model. Other communities want to work through the wider more elaborate process of involving the whole community. And some communities sit somewhere between these two. But no matter what the resources and desired outcomes are, knowing before you begin which model you are attempting adds value to the work that you are attempting. | ||
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