Part of: WPII.1.: Geospatial data assimilation toolbox
Research focus:
❖ To develop and test methods for the integration of EO data from Img2Info with other VGI-generated data (HOT OSM, Missing Maps), user generated data (social media, CDR), survey-based field datasets and pre-existing public / institutional / administrative data (socio-economic data, conflict)
❖ To determine robust data assimilation strategies including quality control protocols, harmonization strategies
Expected results:
❖ Geospatial toolbox incorporating advanced data assimilation techniques from data harvesting and quality control over integration and aggregation to spatial regionalization and modelling
❖ Mapping of settlement structures and areas independent of administrative boundaries in complex urban areas based on the integration of EO-based proxies with other (e.g. VGI-generated) data
❖ Providing population distribution models in urban areas combining geospatial indicators and social media and mobile phone traces
❖ Development of standardized workflows for potential operational usage of MS
Aim: Development of a reliable data assimilation strategy that allows the assessment of complex problems in the humanitarian field, that cannot be directly measured or described by single indicators, but rather need more combined, harmonized information of different data sources.
Specific Objective (SO) II.1.: Finding a robust strategy to combine validated data of different sources with meaningful analysis into aggregated and spatially explicit results including the following steps:
(1) Data harvesting (collection, interfaces to data portals) (M II.1.1.1) & (M II.1.1.2)
(2) Quality control (M II.1.2.1) & (M II.1.2.2)
(3) Data analysis (spatial analyses, interpolation)
(4) Data aggregation (harmonization, normalization, statistical analyses, weighting, aggregation) (M II.1.4.1) & (M II.1.4.2)
(5) Spatial regionalization (M II.1.5.1)
(6) Validation (M II.1.6.1)
Research Questions (RQ) II.1.:
How to find, evaluate, harmonize and modify available data into meaningful indicators?
How to derive a statistically and meaningful sound index to describe specific phenomena (described in WP II.2 and II.3)
and at the same time derive spatially distinct, homogenous areas that reflect these phenomena independent of predefined administrative boundaries?
Part of: WPII.1.: Geospatial data assimilation toolbox
Research focus:
❖ To develop and test methods for the integration of EO data from Img2Info with other VGI-generated data (HOT OSM, Missing Maps), user generated data (social media, CDR), survey-based field datasets and pre-existing public / institutional / administrative data (socio-economic data, conflict)
❖ To determine robust data assimilation strategies including quality control protocols, harmonization strategies
Expected results:
❖ Geospatial toolbox incorporating advanced data assimilation techniques from data harvesting and quality control over integration and aggregation to spatial regionalization and modelling
❖ Mapping of settlement structures and areas independent of administrative boundaries in complex urban areas based on the integration of EO-based proxies with other (e.g. VGI-generated) data
❖ Providing population distribution models in urban areas combining geospatial indicators and social media and mobile phone traces
❖ Development of standardized workflows for potential operational usage of MS
Aim: Development of a reliable data assimilation strategy that allows the assessment of complex problems in the humanitarian field, that cannot be directly measured or described by single indicators, but rather need more combined, harmonized information of different data sources.
Specific Objective (SO) II.1.: Finding a robust strategy to combine validated data of different sources with meaningful analysis into aggregated and spatially explicit results including the following steps:
(1) Data harvesting (collection, interfaces to data portals) (M II.1.1.1) & (M II.1.1.2)
(2) Quality control (M II.1.2.1) & (M II.1.2.2)
(3) Data analysis (spatial analyses, interpolation)
(4) Data aggregation (harmonization, normalization, statistical analyses, weighting, aggregation) (M II.1.4.1) & (M II.1.4.2)
(5) Spatial regionalization (M II.1.5.1)
(6) Validation (M II.1.6.1)
Research Questions (RQ) II.1.: