How do we make complex and not so complex data more accessible to non-technical audiences? Moreover, how do we draw out key messages from data so that the right people can get to grips with the key issues they need to do their job?
Our role as a research team in a local authority is built around these questions. We need to present data in a way that not only gets the audience’s attention, but also leads them to the correct conclusions.
Presenting the data
What can we do to make decision makers sit up and take note and make sure that they base their decisions on sound evidence?
How do we make sure they pay attention to the things that we, as researchers think are important, and that the data is showing us? If the data is presented well, then it should speak for itself. We merely amplify the messages through effective visualization. But how can we do that? In some ways the possibilities are endless.
An example of data visualisation within our work follows.
Delivering a Sustainable Transport System in Leicestershire (DaSTS)
We recently completed a substantial piece of work for our Environment & Transport Department that will inform their 15-year Local Transport Plan 3 (LTP3). The aim was to use some of the analysis and visualization techniques we’ve been developing over the last few years on some of their data sets. The work is theme based and considers how transport planners in Leicestershire can:
- Support economic growth;
- Achieve greater equality of opportunity;
- Improve safety, health and security;
- Tackle climate change; and
- Deliver better quality of life
The full report is available for download at:
Labour market flexibility: A problem of geography and/or local competition?
A particularly current objective for the LTP is to identify how the transport system can improve local economic performance. Spatial economists have argued here that authorities should focus on the way in which transport systems enable flexible labour markets (Gibbons and Machin, 2006). In particular this means understanding the extent to which workers, and especially low-skilled workers, are able to take advantage of local employment opportunities, and whether or not for these people geography acts as a ‘friction’ or obstacle to accessing jobs.
We already knew that in Leicestershire there are several out-of-town business parks offering a number of low-skilled jobs. Most of these are distant from residential areas and, so we thought, possibly inaccessible to low-skilled workers. Such insights were nevertheless largely anecdotal, and the aim of Chapter 4, Economic Growth, was to better understand and represent the size and spatial distribution of jobs and workers in the County. Crucially, we wanted to identify parts of Leicestershire where, either through intense local competition or a lack of locally available opportunities, low-skilled workers were isolated from jobs and low skilled-jobs were distant from workers.
The initial part of the project was relatively straightforward. We wanted to show general spatial patterns of travel-to-work in Leicestershire. The data for this were easily available. We had the origins and destinations (by postcode) of all workers and working residents in Leicestershire. However, our first attempts to represent this information were cluttered and confusing. Instead we used Origin and Destination Maps - a new form a new graphical technique developed by City University’s giCentre - to overcome some of these problems. The upshot was that we were able to see more fully how labour market flows are spatially organised in Leicestershire.
OD maps show connections between sets of origin and destination locations and are used to display travel-to-work flows (from home to work). Drawing lines on a map between individual origins and destinations usually leads to a cluttered visual display and makes it very difficult to see patterns in a data set. Instead in an OD Map, Leicestershire is first divided into 12 grids and each grid cell is then subdivided into a small copy of the larger grid. Within each larger cell, the destination (so the workplace) is emphasised by a thicker boarder and the other finer cells are coloured according to where people have travelled from (so where workers live). Effectively we have 144 destinations within Leicestershire and a map showing the residential areas from which workers have travelled. The darker the colour the larger the number of people travelling to that cell.
This screenshot is taken from the DaSTS report and shows a Patterns of Travel to Work OD map.
The second part of the project - identifying the size and spatial interactions between low-skilled jobs and workers in Leicestershire - was more complicated both to analyse and represent. We found a paper by economists at Warwick University which used a number of measures/metrics for understanding the geography of local labour markets at a local authority level. We adopted the same techniques but at a much smaller level of geography (Output Area, Lower Super Output Area and Ward level). We used metrics such as job ratios and self-containment values to identify levels of in- and out- commuting and, applying a k-means cluster analysis to these data, created a typology of commuting for wards in Leicestershire.
In the final and most significant section of the chapter, we introduced a model which took into account workers’ ability to commute as well as competition for, and distances between, low-skilled workers and jobs.
The data were again cut at ward level. The outcome was that we were able to identify ‘priority’ wards where either distance or intense local competition is likely to drive a wedge between low-skilled workers and jobs; and therefore where regular and cheap transport routes are imperative.
In order to present this data we used spatial-treemap graphics - another new visual display to have emerged out of City University’s giCentre. For a more detailed explanation of how they have been applied to the chapter see the dedicated page on Leicestershire Statistics and Research Online at http://www.lsr-online.org/reports/delivering_a_sustainable_transport_system_in_leicestershire
This paper has recently been accepted at the Infovis Discovery Exhibition at 2010 - an international conference of the world’s leading visualization specialists.
This screenshot is taken from the DaSTS report and shows a spatial treemap depicting the mean distance travelled to workplace by car (the darker the colour the further the distance)
Understanding access to services: Coping with personal circumstances and perceptions, as well as geography
That all residents in the county can easily access services is a major concern for transport planners. The first step in ensuring equality of access is to identify who and where in Leicestershire suffers from poor access. The DfT has developed a useful methodology here which uses a GIS-based network analysis to show geographical access (in terms of travel time and distance) to vital services such as schools, GP surgeries, foodstores and employment centres.
Chapter 5 [http://www.lsr-online.org/reports/delivering_a_sustainable_transport_system_in_leicestershire] of our report, Equality of Opportunity, follows this methodology in identifying actual (geographical) access to key services by a range of demographic groups and types of community. This is useful to an extent, but it tends to overemphasise rural areas where few people live, and where most people are socially and economically mobile. By linking to the Place Survey 2008 data, ours compares this ‘real’ access with residents’ ‘perceived’ access. As a result, we’ve been able to answer questions like: Is poor access only a product of distance? Is it do with personal mobility? Or can it be explained by less obvious things like satisfaction with local services?
Using existing data sets to understand quality of life priorities
With recent high profile fears about the health implications of emissions, Environment and Transport Departments have become increasingly interested in managing air quality. District Councils have set up observation sites which monitor Air Quality. These sites are called Air Quality Management Areas (AQMAs) and are often located in Town Centres or at the intersections of major roads. However, because they are very small areas, little is known about the types of communities/neighbourhoods that lie within them. In Chapter 8 of our report, Quality of Life, [http://www.lsr-online.org/reports/delivering_a_sustainable_transport_system_in_leicestershire] we have taken ground-level estimates of emissions provided by the National Atmospheric Emissions Inventory (NAEI). These data were banded according to statistical breaks and presented at 1km grid square level. Ideally we wanted to understand who - which types of community - in Leicestershire were most likely to be exposed to higher concentrations of emissions. We therefore used the proportional overlay function in GIS to establish the percentage of each Census Area falling within high emissions areas.
Below is a map showing Census Areas in Leicestershire that fall within high emissions areas
The full report, or individual chapters, can be downloaded from Leicestershire Statistics and Research Online.
Page Last Updated: 28 September 2010