Communities Scotland - and its predecessor Scottish Homes
- use a methods called Local Housing System Analysis and Local
Housing Strategies to ascertain the issues facing housing
and regeneration across Scotland's urban and rural communities.
GIS is a key software tool which supports these methods. Why?
The reason is simple - we are concerned with people and place.
The relationship between housing, the availability of services,
economic and other contextual issues is defined by geography:
all these characteristics are inherently geographic.
GIS is used to highlight relationships and identify patterns
through time and space. Other systems (such as statistical
packages) may be used to analyse data, but in many cases geography
is the only common factor between datasets. For this reason
alone GIS is an essential tool.
When one considers the added advantage of analysing data
geographically - by using drivetimes for example - and the
ability to visualise patterns on a map
or as a 3D surface, the power of GIS starts to become
Local Housing System analysis:
"In essence LHSA is about identifying and assessing the nature
of the housing system and patterns of change within it. It
involves a detailed examination of the housing system, principally
households and dwellings. However, it also involves analysing
the external environment to determine which factors are most
likely to influence and impact upon housing outcomes. Examples
of these external factors include economic policy, labour
market conditions, and changing technologies. The intended
outcome of LHSA is to establish what the key housing problems
or issues which require policy action are. Since this analysis
forms the foundation for subsequent decision taking, the quality
and depth of the analysis is of critical importance."
From an old Mapping Awareness
article by Alan Bourke:
Scottish Homes is responsible for allocating its enabling
programme funds across a variety of housing providers, a variety
of housing providers and a complex mixture of urban and rural
housing. Managing the competing priorities of inner and outer
urban investment, partnership areas, rural towns and villages
is a highly complex task.
There is a wealth of socio-economic and housing information
around, but invariably at differing levels of aggregation.
Studies have shown that up to 90 percent of all information
held in public sector organisations is spatial in nature,
is about a place on the ground, and Scottish Homes is no different.
Scottish Homes' business involves using all this information
to help decide where to spend money. The hard part is getting
all the information in one place so each bit can be compared
with all the others. This is where Geographic Information
Systems (GIS) come to the fore.
The GIS is instrumental in allowing users to compare all
this information using the only common factor - location.
Multiple criteria questions can be framed, and results filtered
and clustered to select only areas conforming to predetermined
GIS operates within carefully defined guidelines outlining
best practices for planning resource allocation at both national
and local levels. National housing indicators have been developed
and tested using GIS in line with Scottish Homes' strategic
Such indicators help form a back-cloth of information for
consideration by senior managers when deciding upon the big
picture allocation of resources. Locally, each of Scottish
Homes' districts uses a defined methodology to build up a
picture of the housing system, within the context of the district
planning process. Techniques used in this process are carefully
defined in a "best practice" guide and many are implemented
Testing the self containment of areas in order to help define
'housing market areas' is one application where GIS assists
in the process, as is tracking the resale values of properties
where Scottish Homes has invested. It is often asked whether
this type of work could be done without GIS.
The answer is no. It is the only type of system which allows
users to bring together otherwise incompatible datasets and
analyse them together using what is often their only common
feature - their location.
Consider a typical question: "Show me areas in Scotland with
high proportions of elderly people and for those areas give
me the average house price". Such a question involves bringing
together two otherwise incompatible datasets (census and sasines)
in a way that only GIS can analyse.
For those wishing to do any form of socio-economic analysis
GIS is now no longer an expensive luxury, it is an analytical