Periodic Reporting for period 4 - UrbanOccupationsOETR (Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000)
Reporting period: 2021-04-01 to 2022-09-30
The overarching goal of our project has been to detect and analyze factors of regional differences for economic and demographic development in Southeast Europe and Turkey in the long-run between 1850 and 2000. Our project aims to overcome historiographical and disciplinary limitations in social and economic history, historical geography and urban studies for the Ottoman Empire and the Republic of Turkey. The chosen long-term Ottoman/Turkish perspective is intended to facilitate comparative approaches so as to overcome the limitations of national historiographies. By extending the analysis up to 2000 the project also challenges the disciplinary divide between economic history, economics and urban studies in research on Turkey. To pursue these multiple goals the project has adopted both an inter-disciplinary approach and a comparative perspective. Throughout the project the focus has been on regional dynamics of industrialisation, urbanisation and their accompanying changes in occupational structures and residential and migration patterns.
The main achievement in the first half of our project was to build capacities in accordance with, and to operate within, the field of digital humanities. In areas of data extraction, curation, dataset construction and data analysis we have developed state-of-art, customised tools and methods. We also made extensive use of geographic information systems (GIS) applications and created bespoke tools for historical analysis in the field of historical GIS / geospatial humanities.
Our project mostly relies on two types of historical sources: a) mid-nineteenth century micro level archival sources, which convey minutely detailed demographic and economic data by individuals and households; b) published censuses conducted by the Ottoman state and its successor states (Turkey and several Southeast European countries). At the heart of our research structure we had this tension of bridging pre-census micro level individual data and census-era tabulated and aggregated data on demography and economic life for a large territory. By devising our geosampling methods tailored to our needs we are confident that we have managed to reach commensurability between pre-census and census era data to construct several longitudinal datasets which has one of the main aims of UrbanOccupationsOETR.
We think our solutions to bridge pre-census micro level individual historical data with tabulated categorized census data in a commensurable manner, has implications not only for our project and the field of history, but also for several other discipline and digital and geospatial humanities projects.
The main achievement in the first half of our project was to build capacities in accordance with, and to operate within, the field of digital humanities. In areas of data extraction, curation, dataset construction and data analysis we have developed state-of-art, customised tools and methods. We also made extensive use of geographic information systems (GIS) applications and created bespoke tools for historical analysis in the field of historical GIS / geospatial humanities.
Our project mostly relies on two types of historical sources: a) mid-nineteenth century micro level archival sources, which convey minutely detailed demographic and economic data by individuals and households; b) published censuses conducted by the Ottoman state and its successor states (Turkey and several Southeast European countries). At the heart of our research structure we had this tension of bridging pre-census micro level individual data and census-era tabulated and aggregated data on demography and economic life for a large territory. By devising our geosampling methods tailored to our needs we are confident that we have managed to reach commensurability between pre-census and census era data to construct several longitudinal datasets which has one of the main aims of UrbanOccupationsOETR.
We think our solutions to bridge pre-census micro level individual historical data with tabulated categorized census data in a commensurable manner, has implications not only for our project and the field of history, but also for several other discipline and digital and geospatial humanities projects.
For the Ottoman archival documentation, we have recruited data scientists and developed our own expertise and capacity, to build a customised data infrastructure for the project. The challenge in this endeavour has been to design, create, constantly modify and update data entry templates in the front end for data inputters; and organise the dataset architecture at the back end; for data analysis. We opted for a relational database structure using Microsoft Access. The difficulty in using this and similar relational databases for data entry for historical sources is finding a method to customise data entry for multiple users without compromising data entry speed or accuracy. To reach this goal we, where possible partially automated, and semi-automated data entry. Having a permanent member in the team solely responsible for data entry, maintenance, and safe keeping enabled us to develop the digital research infrastructure we have been aiming for. With increased speed and reliance, we managed to expand the geographical territory for which we have been collecting demographic data from Ottoman population and economic data from Ottoman tax registers for the mid-nineteenth century considerably. Based upon the premise that Ottoman economic and demographic structure varied considerably in the 1840s, which build the base period for our long-term perspective, in order to be able to make inter- and intraregional comparisons, we have developed a regional comparative perspective and a concomitant data extraction principle and a three-tier regional method. First, we locate an urban centre as a primary location which is suitable to analyse long-term dynamics of industrialisation and urbanisation, which are the two main axes of our project. Secondly, we look for three towns (secondary locations) in the surroundings of that city and set boundaries for a region via including all villages (tertiary locations) in that region. In designing this three-tier regional perspective we qualify and disqualify possible candidates for primary and secondary locations via operating under multiple constraints: i.e. primary and secondary locations should be covered by the 1840s Ottoman population and tax registers. By focusing on primary and secondary locations we can analyse urban economic and demographic changes by bringing in selected urban census data.
After geolocating primary and secondary locations using GIS then we take the arduous task to find and geolocate hundreds of tertiary villages in our chosen regions which are listed in the 1840s Ottoman population registers with total number of households and males in ethno-religious sub-categories per village. After geolocating the tertiary locations, we finalise the boundaries for a region. In doing so we can create unprecedented and reliable population density maps of Ottoman regions preceding to population censuses. We use historical maps from census years to harvest shapefiles to be populated by locations and demographic data for villages prior to censuses. Furthermore, since our regions are delineated via GIS we can also calculate population densities beyond two-dimensional space and calculate surfaces of our regions using a Digital Elevation Model. These geospatial aspects allow us to calculate more realistic population densities by setting elevation limitations to settlements. We have digitised, spatially harmonised, and tagged data with historical maps corresponding to census years from population censuses of countries of Southeast Europe and Turkey using optical character recognition software and GIS. We have almost completed all censuses for Turkey for 1927-2000 and most of Bulgarian censuses for 1880s-1956 to acquire project specific demographic and occupational data at highest possible spatial resolution. Bringing pre-census and census data on regional scale allowed us to examine regional economic development in the long run.
After geolocating primary and secondary locations using GIS then we take the arduous task to find and geolocate hundreds of tertiary villages in our chosen regions which are listed in the 1840s Ottoman population registers with total number of households and males in ethno-religious sub-categories per village. After geolocating the tertiary locations, we finalise the boundaries for a region. In doing so we can create unprecedented and reliable population density maps of Ottoman regions preceding to population censuses. We use historical maps from census years to harvest shapefiles to be populated by locations and demographic data for villages prior to censuses. Furthermore, since our regions are delineated via GIS we can also calculate population densities beyond two-dimensional space and calculate surfaces of our regions using a Digital Elevation Model. These geospatial aspects allow us to calculate more realistic population densities by setting elevation limitations to settlements. We have digitised, spatially harmonised, and tagged data with historical maps corresponding to census years from population censuses of countries of Southeast Europe and Turkey using optical character recognition software and GIS. We have almost completed all censuses for Turkey for 1927-2000 and most of Bulgarian censuses for 1880s-1956 to acquire project specific demographic and occupational data at highest possible spatial resolution. Bringing pre-census and census data on regional scale allowed us to examine regional economic development in the long run.
We would like to refine our geosampling method and further sophisticate our geospatial analysis and tools especially for historical soil and land use data.
We have been working on devising a historical multi-modal transport network, which we would like to test and improve its accuracy.
We have been working on devising a historical multi-modal transport network, which we would like to test and improve its accuracy.