Scalable Analytical Framework for Spatio-temporal Data Analysis
Lead Research Organisation:
University of Liverpool
Department Name: Geography and Planning
Abstract
There are many applications for spatio-temporal data analysis for solving real-world problems. In the context of a Big Data system, spatio-temporal data often utilized along with other types of data. In Big Data world, generally there are two computational Analytical tasks in processing and analyzing spatio-temporal data (data preparation and cleaning is not considered as a computational task in this proposal). The first task is spatial-temporal data engineering and exploratory data analysis, that is about using various methods, operators and predicates to transform spatial data into a shape suitable for further analysis. In addition, the spatio-temporal analysis might generate a new set of spatio-temporal data product. It may include descriptive and exploratory data analysis. The data engineering and exploratory tasks are most of the time important initial steps in further complex spatio-temporal data analysis. In addition, descriptive and exploratory data analysis often are all that can be done in complex datasets. The second task is about using geostatistics, statistical learning, machine learning, simulation, etc. to model a process, event, activity, behavior, etc. which often results in describing or predicting a process, activity, event, behavior. While having major importance in many types of Analytics, most spatio-temporal data analysis suffer from lack of scalability. The Lack of scalability prohibit utilizing spatio-temporal analysis along with other new types of data in a big data ecosystem. This is why there is no major open source and reliable big data technology for spatio-temporal analysis. In addition, some applications of spatio-temporal data need real-time or near real-time response. Therefore existing spatio-temporal data analysis need to be integrated into existing big data platform (via importing and modifying the source code, redesign of algorithms, etc.). More importantly, the scalable spatio-temporal analytical framework needs to be tested and validated with a large set of real-world data. An objective of this proposal is to create a scalable framework for spatio-temporal analysis. The framework also can act as a guide for making methods of spatial analysis scalable. Another objective of this proposal is that the developed framework must be tested and validated for a real-world application possibly. This can be a specific application like disaster management, or resource targeting.
Organisations
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ES/P000401/1 | 01/10/2017 | 30/09/2024 | |||
1949039 | Studentship | ES/P000401/1 | 25/09/2017 | 24/09/2021 | Nikolaos Patias |
Description | This work presents a scalable analytical framework for understanding spatio-temporal neighbourhood change. In the first part, the focus is on population change, using a sequence analysis approach. We use gridded cell Census data for Great Britain from 1971 to 2011 with 10-year intervals, creating neighbourhood typologies for each Census year. These typologies are then used to analyze transitions of grid cells between different types of neighborhoods and define representative trajectories of neighborhood change. The results reveal seven prevalent trajectories of neighbourhood change across Great Britain, identifying neighbourhoods which have experienced stable, upward and downward pathways through the national socioeconomic hierarchy over the last four decades. In the second part, the focus is on change in built environment urban structure. Urban structure has been proved to be an important factor guiding urban smart growth policies that promote sustainable urban environments and improve neighbourhood social cohesion. This piece of work draws on a series of unique historical datasets obtained from Ordnance Survey, covering the largest British urban areas over the last 15 years (2001-2016) to develop a set of twelve indicators and a composite Sustainable Urban Development Index to establish the spatial and temporal structure of changes in urban structure. The results show that there is a uniform increase in urban structure sustainability of areas in and around city centres and identify that the primary built environment feature driving these improvements was an increase in walkable spaces. |
Exploitation Route | The outcome of this project is relevant to public policy, particularly to policies focusing on long term socioeconomic inequalities. The UK stands out as one of the most unequal countries in the industrialised world. Yet, little is known about the local structure of these inequalities. Hence, the results of this work offer the opportunity to understand long term socioeconomic inequalities at small geographic level (i.e. grids). Finally, the grids can be aggregated at different geographic levels so to understand iintra-regional and inter-urban inequalities at the neighbourhood level in Britain. |
Sectors | Communities and Social Services/Policy,Environment,Healthcare,Retail,Transport |
URL | https://link.springer.com/chapter/10.1007/978-3-030-14745-7_13 |
Description | Part of my findings have been used in the first report of the UK2070 commission. The UK2070 Commission is an independent inquiry into the deep- rooted spatial inequalities in the United Kingdom. |
First Year Of Impact | 2019 |
Sector | Communities and Social Services/Policy,Healthcare |
Impact Types | Societal,Economic,Policy & public services |
Title | extRatum: Summary Statistics for Geospatial Features |
Description | Provides summary statistics of local geospatial features within a given geographic area. It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). The geospatial features can be of any type of geospatial data, including point, polygon or line data. |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | It can be used for geospatial data cleaning or/and to create simple indicators for built environment characteristics. This tool can be used to track performance of different areas across local areas, regions or countries. |