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Big Data – Good Data: Understanding Society by combining survey data and new forms of data

School of Geographical and Earth Sciences

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Prof Ana Basiri No more applications being accepted Funded PhD Project (Students Worldwide)
Glasgow United Kingdom Human Geography Environmental Sciences Urban Planning

About the Project

Social Science: Human Geography, Environment and Urban Planning

User-generated data, e.g. social media and crowdsourced data, have provided researchers and decision makers with an unprecedented opportunity to monitor and understand society at much higher frequency and granularity. However, these ‘new forms of data’ may have some challenges to be considered alongside traditional data, such as randomised surveys. Their huge sample size and questionable quality (e.g. bias, dependencies, representativeness, and missingness) make inferences from traditional statistical and analytical methods questionable. Adjustments are thus needed to ensure biased and unrepresentative data do not lead to flawed conclusions. This project builds on recent work by incorporating geospatial and social components to two main challenges of using user-generated data in social science, i.e. quality-quantity balance, and missing data imputation.

  • This project builds on recent work by incorporating geospatial and social components to:
  • understand the effective sample size of “big data”, data with geospatial and social components,
  • provide approaches to combine new forms of data with survey data,
  • provide mechanisms to impute missing data (Missingness Not At Random (MNAR)) using areal-level effects and identify the functions that links the missingness and the values (e.g. low income) at an aggregated level.

By considering spatiality and connectivity, through network autocorrelation, the proposed work will allow new forms of data to be combined with survey data (Scottish Household Survey, Understanding Society, and National Travel Survey) to make valid inferences about society at a higher frequency and lower cost than has previously been possible.

This project will provide a geospatial and social science-enabled solution to the challenge of “big data paradox” and missing data which will enable wider academic disciplines, including computer science, data science and artificial intelligence, decision makers and policymakers to reliably use user-generated data, and traditional survey data to have a meaningful, realistic and and statistically valid data-driven results.

Applicants must meet the following eligibility criteria:

Entry Requirements:

  • 2.1 Honours degree or equivalent


For applicants whose first language is not English, the University sets a minimum English Language proficiency level.

International English Language Testing System (IELTS) Academic module (not General Training) or equivalent English language qualifications:

  • 6.5 with no sub-test under 6.0.
  • Tests must have been taken within 4 years 5 months of start date. Combined scores from two tests taken within 6 months of each other can be considered.

Please note that all applicants must also meet the ESRC eligibility criteria. ESRC eligibility information can be found here.

For full details and to apply for this studentship, please visit the Scottish Graduate School of Social Science (SGSSS) website here.

Applications will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview by 9th April 2021. Interviews will take place on 16th April 2021.

Funding Notes

The scholarship is available as a +3 (3 year PhD) or a 1+3 (Masters year and 3 year PhD) studentship depending on prior research training (this will be assessed as part of the recruitment process). The programme will commence in October 2021 and the full ESRC studentship package includes, as advised by ESRC:
• An annual maintenance grant (stipend)
• Fees at the standard institutional home rate
• Students can also draw on a pooled Research Training Support Grant (RTSG)


Applicants will be asked to provide contact details for two referees during the application process. SGSSS will then contact them (automatically) and ask that they each complete a reference template and return this to applicants intended institution. Applicants should seek permission from their referees in advance of providing their details to SGSSS.

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