The efficient management of the project will be secured through weekly Meetings with the supervisor, Quarterly progress reports, and Interim and Final reports.
The project’s methodology relies on population data extraction, settlement identification and georeferencing of this data. Completion of each phase of data collection is regarded as a Milestone.
Milestones: Identification of the 19th-century nüfus registers in the BOA, data extraction, settlement identification, and creation of settlement data points (M2.1); identification of the 18th-century kefalet registers in the BOA and data extraction (M2.2); population data extraction from Bulgarian national censuses (M2.3).
The training program of the project envisages custom-designed training in advanced GIS methods, training in advanced methods of data science, and a continuous process of training through interaction with the supervisor and the team members of the UrbanOccupations_OETR project.
Georeferenced data collected in WP2 will be subjected to precise spatial analysis that will result in the following Deliverables:
Deliverables: Creation of historically accurate administrative polygons, based on the 19th-century registers (D4.1); Spatial analysis of previously collected 16th–17th- and 19th-century data (D4.2); Creation of historically accurate administrative polygons, based on the 18th-century registers and 20th-century population censuses (D4.3); Spatial and cluster analysis of Bulgaria’s population household structure (D4.4)
The dissemination plan of the project results includes academic publications, presentations at conferences, organization of a workshop, public talks, launch of openly accessible online platforms that structurally visualize the DB, and integration of the project’s DB into the datasets of CAMPOP.
The dissemination plan of the project results includes academic publications, presentations at conferences, organization of a workshop, public talks, launch of openly accessible online platforms that structurally visualize the DB, and integration of the project’s DB into the datasets of CAMPOP.