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Surface Groups Mauritania 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Peru 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups French Guiana 1984-2023
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Paraguay 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Bermuda 1984-2023
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Jordan 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Tunisia 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Venezuela 1984-2023
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Career Tracker Cohorts (CTC)
No full textThe SNSF Career Tracker Cohorts (CTC) study tracks the careers of applicants for the postdoctoral career funding schemes of the Swiss National Science Foundation (SNSF). These include Early Postdoc.Mobility, Postdoc.Mobility, Ambizione, PRIMA, and Eccellenza. The aim of the CTC study is to gain a better understanding of the researchers’ career paths and of the career impact that is attributable to the SNSF career funding schemes. The results will also serve as a basis for the future development of career funding policies and schemes at the SNSF. The CTC project is designed as a panel study with yearly cohorts. Every new cohort starts with a base survey shortly after the application deadline. Subsequently, the participants are invited to take part in a monitoring survey every year, in order to follow-up on their professional and personal life situations
Q-Guide: example for a paper publication (DOC ONLY)
No full textThe preparation and reporting of qualitative research require transparent values and consistent procedures to produce well-crafted, credible results. Such transparent reporting is especially important in the context of epistemic pluralism, as it aids in explicating theoretical commitments and their methodological implications. To this end, this paper develops a guide – called the Q-Guide – comprising 15 dimensions for preparing and reporting qualitative research, grounded in the three values of review, reflexivity and responsibility. It considers three established guides for reporting qualitative research in the social sciences and adapts them to methodological debates in different language traditions in geography to propose the Q-Guide. The Q-Guide provides a systematic framework of aspects to consider in preparing and reporting qualitative research. The paper provides the Q-Guide as a downloadable tool and two examples of its application to guide methods reporting. Adoption of the Q-Guide supports more consistent and transparent methods reporting, enabling more reflexive and responsible research