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
- The features that distinguish the model from others,
- The conditions that make use of the model most likely, and
- The pros and cons of using the model.
To make each of the models as real as possible, the paper also describes how the model actually
worked within each community.
Before describing the three models of building a data warehouse, the paper outlines
- The relationship between creating healthy communities – which is the goal of the
nonprofit sector – and the building of data warehouses, and
- The end product or data warehouse that each of the three models works to build.
Relationship between creating healthy communities and building data warehouses
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:
- Types of Data
- Types of Interface
- Levels of Access
- Groupings of Community
Types of Data
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.
