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Big Data & Spatial Analytics

Thursday, 22 Oct 2015 (revised date: Tuesday, 27 Oct 2015)
Sayara Beg

Big Data and Spatial Analytics

Venue: The Royal Statistical Society, 12 Errol Street, London, EC1Y 8LX

Speaker: Claudia Vitolo (Imperial College) & Daqin Chen (London South Bank University)

Date: Wednesday, 18 November 2015 at 16:00 - 18:00

Big Data & Spatial Analytics

16:00 - 16:45 - Claudia Vitolo (Imperial College) "Improving access to geospatial Big Data in the hydrology domain"

There is a trend of increasing transparency, based on which information produced at public expense is gradually been made open and freely available to improve public involvement in the process of decision and policy making. For hydrologists, this translates into free access to an increasing volume of climate and hydrometric information. However, "free access" is not synonymous of "easy access" and there are many challenges arising when trying to obtain information for hundreds of sites. This talk will focus on the need for interfaces to facilitate programmatic access to data, also touching on reproducible processing workflows and efficient use of distributed computing facilities.

16:45 - 17:00 - Coffee Break

17:00 - 17:45 - Daqing Chen (London South Bank University) "Big Data Analytics Ssytem for Fact/Data-driven Decision Making'

Big Data analytics system has been developed for the London Borough of Lambeth to assist its activities in fact/data driven decision making. The system integrates various historical data from both internal and external sources to provide timely and accurate profiles on multiple measures that the borough council would be concerned.

The data integrated from multiple sources includes demographics (Census: Population, ethnicity, disability, gender, tenure, household composition), employment (NOIMS, census, and annual business survey: Working age, unemployment by age and gender, employment by industry, year-on-year change in unemployment, NEETs), deprivation (NOIMS, IMD, census, and school survey: Child and working tax credits, free school meals, IMD scores, DLA claimants), health care (Health stats: Childhood obesity, percentage of people with respiratory problems, number of hospital admissions, live expectancy, deaths), transport ( Road accident instances, year-on-year change in road accident instances), crime (Met police: Average monthly total crimes, year-on-year change in total crimes), etc.

The information is presented in an interactive, hierarchical and comparative way by using a map-based visualization dashboard. For a given measure, the borough average value can be obtained and compared with the value of each ward/LSOA code within the borough. The system is available in both Tableau-based and SQL Server-based platforms.

17:45 - 18:00 - Discussion & Networking

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