Spatial planning of public charging points using multi-dimensional analysis of early adopters of electric vehicles for a city region

The success of a mass roll out of Plug-in electric vehicles (PEVs) is largely underpinned by establishment of suitable charging infrastructure. This paper presents a geospatial modelling approach, exploring the potentials for deployment of publicly accessible charging opportunities for consumers based on two traits — one, trip characteristics (journey purpose and destinations); two, PEV adoption intensity. Its applicability is demonstrated through a case study, which combines census statistics indicating lifestyle trends, family size, age group and affordability with travel patterns for an administrative region in the North-East England. Three categories of potential PEV users have been identified — ‘New Urban Colonists’, ‘City Adventurers’ and ‘Corporate Chieftains’. Analysis results indicate that Corporate Chieftains, primarily residing in peri-urban locations, with multi-car ownership and availability of onsite overnight charging facilities form the strongest group of early adopters, irrespective of public charging provision. On the other hand, New Urban Colonists and City Adventurers, primarily residing in the inner-city regions, show potentials of forming a relatively bigger cohort of early PEV adopters but their uptake is found to be dependent largely on public charging facilities. Our study suggests that effective PEV diffusion in city-regions globally would require catering mainly to the demands of the latter group, focussing on development of a purpose-built public charging infrastructure, both for provision of on-street overnight charging facilities in residential locations and for fast charging at parking hubs (park and ride, amenities and commercial centres)

Deprivation Scores Based on 1991 and 2001 Census Area Statistics

Abstract copyright UK Data Service and data collection copyright owner.These deprivation scores are based on those created by Townsend, Morris, Carstairs, Jarman and the Department of the Environment (DoE). Calculating measures of deprivation is perhaps one of the commonest uses of census data. Data on, for example, housing, employment, social class and availability of cars are used to create a single measure of area deprivation. There are various measures of deprivation that use different census variables or give different weights to the same variables. In addition, the Deprivation data webpage at UK Data Service Census Support provides more information and links to related measures available elsewhere. These data are restricted to staff and students from UK further/higher education institutions. If your intended use of the data involves partnership with or funding from any non-academic organisations or individuals, or if you are uncertain about whether your use of the data is entirely academic, please Get in touch. Main Topics:The following deprivation scores can be downloaded: 2001 Carstairs and Townsend Scores1991 Carstairs Scores1991 Townsend Scores1991 DoE, Jarman, Carstairs and Townsend Scores</ul

Deprivation Scores Based on 1991 and 2001 Census Area Statistics

<p>Abstract copyright UK Data Service and data collection copyright owner.</p>These deprivation scores are based on those created by Townsend, Morris, Carstairs, Jarman and the Department of the Environment (DoE).<br> <br> Calculating measures of deprivation is perhaps one of the commonest uses of census data. Data on, for example, housing, employment, social class and availability of cars are used to create a single measure of area deprivation. There are various measures of deprivation that use different census variables or give different weights to the same variables.<br> <br> In addition, the <a href="http://census.ukdataservice.ac.uk/get-data/related/deprivation.aspx" title="Deprivation data">Deprivation data</a> webpage at UK Data Service Census Support provides more information and links to related measures available elsewhere.<br> <br> These data are restricted to staff and students from UK further/higher education institutions. If your intended use of the data involves partnership with or funding from any non-academic organisations or individuals, or if you are uncertain about whether your use of the data is entirely academic, please <a href="http://ukdataservice.ac.uk/help/get-in-touch.aspx" title="Get in touch">Get in touch</a>.<br> <br><B>Main Topics</B>:<br>The following deprivation scores can be downloaded: <br> <ul><li>2001 Carstairs and Townsend Scores</li><li>1991 Carstairs Scores</li><li>1991 Townsend Scores</li><li>1991 DoE, Jarman, Carstairs and Townsend Scores</li></ul

Geographic correlation between deprivation and risk of meningococcal disease: an ecological study

<p>Abstract</p> <p>Background</p> <p>Meningitis caused by <it>Neisseria meningitidis </it>is a serious infection which is most common in young children and adolescents. This study investigated the relationships between the incidence and age distribution of meningococcal disease, and socioeconomic environment.</p> <p>Methods</p> <p>An ecological design was used, including mapping using a Geographical Information System (GIS) at census ward level.</p> <p>Results</p> <p>Incidence of meningococcal disease was highest in the most deprived wards, with a relative risk of 1.97 (1.55 – 2.51). Mapping revealed geographical coincidence of deprivation and meningococcal disease, particularly in urban areas. Two-thirds of the increased incidence was due to cases in the under fives.</p> <p>Conclusions</p> <p>The results suggest that area deprivation is a risk factor for meningococcal disease, and that its effects are seen most in young children.</p