Three models of constructing GIS tools in the non-profit sector
Types of Interface
There are three ways that the data is provided to the user of the data warehouse:
- Statistics
- Maps
- Social Indicators
Statistics – The first way that the data is provided to the user is through statistics. The data is
presented in a series of tabs. Each tab refers to a set of data. For example, the Children’s Aid
data tab has data contributed by Children’s Aid on the sexual abuse, physical abuse, neglect,
children in care, families needing services, etc. This data is presented in a table format using
actual numbers (much like any spreadsheet of data). The user has the option to use a number of
statistical tools on the data such as average, percent, deviation, range, etc. The user can also look
at the same information in the form of a pie chart or bar graph. In the cases where longitudinal
data is available, the user can also examine the trends in the data.
Maps – The second way that the data is provided to the user is through maps. The same set of
tabs of data as were in the statistics section are presented, this time with a mapping component
instead of a spreadsheet. Using the same example of Children’s Aid data, the Children’s Aid
decides the most compelling map that they want to use to display their data. This pre-created
map acts as a portal to a fully interactive GIS interface that allows the user to plot whatever
pieces of data are relevant to them. As well as the tabs of data available through statistics, there
are also several tabs of specifically spatial data such as the location of social services, municipal
services, roads, etc.
Social Indicators – The final way that the data is provided to the user is through a series of social
or quality of life indicators. The data contained in the various data sets is sifted, sorted, weighed
and shaped into a set of meaningful indicators that say something about the community, such as
livability, safety, diversity, poverty, etc. For example, the indicator livability takes a number of
pieces of data such as amount of green space, number of street lights, number of vacant
buildings, number of potholes, population density, availability of basic services within walking
distance, etc. and weighs them to create a single index of livability. This type of interface allows
neighborhoods to set and track goals for community well-being.
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 public level
- The interactive level
- The secure level
The public level contains data that is normally available to the public in report form. This
information includes census data, public reports from health, social services, the school board,
and other public organizations. The information is accessible to the general public. The general
public will be able to view the material electronically and perform analyses on publicly available
information.
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:
- Lots of soft connections into community – people who are confident that they know what
the community can use, and are capable of implementing it;
- Technical expertise – both in terms of the software (databases, GIS, etc) and in terms of
community data and needs;
- Limited resources;
- Pre-established focus.
The Pros
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.
