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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of HistoryIndexed at: search.filters.indexed.PubMed

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    A nineteenth-century urban ottoman population micro dataset: data extraction and relational database curation from the 1840s pre-census bursa population registers
    (Nature Portfolio, 2024) Department of History; Kabadayı, Mustafa Erdem; Erünal, Efe; Department of History; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities
    In recent decades, the "big microdata revolution" has transformed access to transcribed historical census data for social science research. However, the population records of the Ottoman Empire, spanning Southeastern Europe, Western Asia, and Northern Africa, remained inaccessible to the big microdata ecosystem due to their prolonged unavailability. This publication marks the inaugural release of complete population data for an Ottoman urban center, Bursa, derived from the 1839 population registers. The dataset presents originally non-tabulated register data in a tabular format integrated into a relational Microsoft Access database. Thus, we showcase the extensive and diverse data found in the Ottoman population registers, demonstrating a level of quality and sophistication akin to the censuses conducted worldwide in the nineteenth century. This valuable resource, whose potential has been massively underexploited, is now presented in an accessible format compatible with global microdata repositories. Our aim with this dataset is to enable historical demographic studies for the Ottoman realm and beyond, while also broadening access to the datasets constructed by our large research team.
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    CNN-based page segmentation and object classification for counting population in ottoman archival documentation
    (Multidisciplinary Digital Publishing Institute (MPDI), 2020) Department of History; N/A; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Researcher; Department of History; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 33267; N/A
    Historical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.
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    Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858-2020
    (Nature Publishing Group (NPG), 2022) Ettehadi Osgouei, Paria; Sertel, Elif; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267
    Land surface of the Earth has been changing as a result of human induced activities and natural processes. Accurate representation of landscape characteristics and precise determination of spatio-temporal changes provide valuable inputs for environmental models, landscape and urban planning, and historical land cover change analysis. This study aims to determine historical land use and land cover (LULC) changes using multi-modal geospatial data, which are the cadastral maps produced in 1858, monochrome aerial photographs obtained in 1955, and multi-spectral WorldView-3 satellite images of 2020. We investigated two pilot regions, Aksu and Kestel towns in Bursa/Turkey, to analyze the long-term LULC changes quantitatively and to understand the driving forces that caused the changes. We propose methods to facilitate the preparation of historical datasets for the LULC change detection and present an object-oriented joint classification scheme for multi-source datasets to accurately map the spatio-temporal changes. Our approach minimized the amount of manual digitizing required for the boundary delineation of LULC classes from historical geospatial data. Also, our quantitative analysis of LULC maps indicates diverging developments for the selected locations in the long period of 162 years. We observed rural depopulation and gradual afforestation in Aksu; whereas, agricultural land abandonment and deforestation in Kestel.