Showing 17-22 of 22 results
IAB-SMART-Mobility

IAB-SMART-Mobility

Stefan
Open Dataset
Creators: Research Data Centre of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB)
Publication Date: 2023
Creators: Research Data Centre of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB)

The IAB-SMART Mobility data module provides smartphone-generated mobility indicators as additional variables for the Panel Labor Market and Social Security (PASS). Participants in IAB-SMART are PASS respondents from wave 11 (2017) with an Android smartphone who were invited to install the IAB-SMART app.  Provided the user’s consent, the IAB-SMART app collected the location of the smartphones at half-hour intervals from January to August 2018. Mobility indicators such as the number of visited places or the share of location measurements at home were derived from this location information. The mobility indicators capture the entire observation period as well as weekdays and weekends separately. With IAB-SMART Mobility, research questions related to mobility behaviour concerning employment and unemployment can be analysed. The indicators from IAB-SMART Mobility complement the survey data from PASS and can also be linked with the administrative data from IAB (PASS-ADIAB). More Information can be found at the website of the FDZ-BA/IAB: DOI: 10.5164/IAB.IAB-SMART.de.en.v1

Detailed information about the data module, as well as a guide on how to link IAB-SMART Mobility with PASS data, can be found in the following publication: Zimmermann, Florian, Filser, Andreas, Haas, Georg-Christoph; Bähr, Sebastian (2023). The IAB-SMART-Mobility Module: An Innovative Research Dataset with Mobility Indicators Based on Raw Geodata. Jahrbücher für Nationalökonomie und Statistik. https://doi.org/10.1515/jbnst-2023-0051

Central African Republic: Displacement Data - Baseline Assessment

Central African Republic: Displacement Data - Baseline Assessment

Stefan
Open Dataset
Creators: International Organization for Migration (IOM)
Publication Date: 2024
Creators: International Organization for Migration (IOM)

This Data is about IDP, returnees from CAR (previous IDP) and returnees from other countries repartition by origin and period of displacement and between 2013 and the date of assessment. It offers data on the distribution of IDPs and returnees, including their origins, periods of displacement, and reasons for displacement. This information is crucial for understanding the dynamics of displacement within CAR and for planning humanitarian interventions. As a sub-component of mobility tracking, the baseline assessment collects data on the presence of displaced populations in defined geographic areas. This foundational data supports the identification of needs and the coordination of assistance efforts. Evaluation has been run in 6 prefectures (admin1), 16 sub-prefectures (admin2) and 367 localities. The number of observations varies across different rounds of data collection. For instance, Round 6 of the assessment identified a total displaced population of 1,074,983 individuals, comprising 580,692 IDPs and 375,684 returnees. The dataset is 16,7 kB and organized into several key components:​

  • IDP Data: Information on internally displaced persons, including their locations, demographics, and displacement periods.​

  • Returnee Data: Details about returnees, both former IDPs and those returning from other countries, including their areas of origin and return timelines.​

  • Reasons for Displacement: Categorization of displacement causes, such as armed conflicts, inter-community tensions, or preventive measures. Notably, 67% of internal displacements are linked to armed conflicts, 26% to inter-community tensions, and 6% are preventive. ​

  • Geographical Distribution: Data on the distribution of displaced populations across various regions and localities within CAR.

Africapolis data

Africapolis data

Stefan
Open Dataset
Creators: OECD; SWAC
Publication Date: 2022
Creators: OECD; SWAC

Africapolis has been designed to provide a much needed standardised and geospatial database on urbanisation dynamics in Africa, with the aim of making urban data in Africa comparable across countries and across time. This version of Africapolis is the first time that the data for the 54 countries currently covered are available for the same base year — 2015. In addition, Africapolis closes one major data gap by integrating 7,496 small towns and intermediary cities between 10,000 and 300,000 inhabitants. Africapolis data is based on a large inventory of housing and population censuses, electoral registers and other official population sources, in some cases dating back to the beginning of the 20th century. The dataset has a size of 34,5 kB and contains the following information:

  • Spatial Data: Urban agglomerations are represented as polygon vector data, delineating the spatial extent of settlements. Each polygon’s size corresponds to the settled area, providing insights into urban sprawl and density. ​

  • Attribute Data: Each spatial unit is linked to demographic attributes, such as total population figures for specific years (e.g., 2015, 2020). This linkage enables analyses of population distribution and urban growth patterns. ​

  • Data Layers: The dataset includes multiple layers corresponding to different years, supporting temporal analyses of urbanization trends.

Computer generated building footprints for the United States

Computer generated building footprints for the United States

Stefan
Open Dataset
Creators: Microsoft Bing Maps Team
Publication Date: 2018
Creators: Microsoft Bing Maps Team

Microsoft Maps is releasing country wide open building footprints datasets in United States. This dataset contains 129,591,852 computer generated building footprints derived using our computer vision algorithms on satellite imagery. Building footprints were extracted using deep neural networks for semantic segmentation, followed by polygonization to convert detected building pixels into vector shapes. This data is freely available for download and use. The dataset is organized by U.S. state and provided in GeoJSON format. Each GeoJSON file contains polygon geometries representing building footprints, accompanied by metadata such as the capture date of the underlying imagery. Notably, footprints within specific regions are based on imagery from 2019-2020, accounting for approximately 73,250,745 buildings.

CO2 emissions and ancillary data for 343 cities from diverse sources

CO2 emissions and ancillary data for 343 cities from diverse sources

Stefan
Open Dataset
Creators: Nangini, Cathy; Peregon Anna; Ciais, Philippe; Weddige, Ulf; Vogel, Felix; Wang, Jun; Bréon, François-Marie; Bachra, Simeran; Wang, Yilong; Gurney, Kevin; Yamagata, Yoshiki; Appleby, Kyra; Telahoun, Sara; Canadell, Josep G; Grübler, Arnulf; Dhakal, Shobhakar; Creutzig, Felix
Publication Date: 2019
Creators: Nangini, Cathy; Peregon Anna; Ciais, Philippe; Weddige, Ulf; Vogel, Felix; Wang, Jun; Bréon, François-Marie; Bachra, Simeran; Wang, Yilong; Gurney, Kevin; Yamagata, Yoshiki; Appleby, Kyra; Telahoun, Sara; Canadell, Josep G; Grübler, Arnulf; Dhakal, Shobhakar; Creutzig, Felix

This dataset collects anthropogenic carbon dioxide emissions data, supplemented with various socio-economic and environmental factors, across 343 cities worldwide. A dataset of dimensions 343 × 179 consisting of CO2 emissions from CDP (187 cities, few in developing countries), the Bonn Center for Local Climate contains action and reporting data (73 cities, mainly in developing countries), and data collected by Peking University (83 cities in China). Further, a set of socio-economic variables – called ancillary data – were collected from other datasets (e.g. socio-economic and traffic indices) or calculated (climate indices, urban area expansion), then combined with the emission data. The remaining attributes are descriptive (e.g. city name, country, etc.) or related to quality assurance/control checks. The file size is 1,8 MB and the majority (88%) of the cities reported emissions between 2010 and 2015. Structurally the dataset contains

  • City Identification: Each entry includes city name, country, and other descriptive attributes.​
  • CO₂ Emissions Data: Reported emissions with quality assurance/control checks.​
  • Ancillary Variables: Socio-economic data, traffic indices, climate indices, urban area expansion metrics, and more.​

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Facebook Privacy-Protected Full URLs Data Set

Facebook Privacy-Protected Full URLs Data Set

Stefan
Open Dataset
Creators: Messing, Solomon; DeGregorio, Christina; Hillenbrand, Bennett; King, Gary; Mahanti, Saurav; Mukerjee, Zagreb; Nayak, Chaya; Persily, Nate; State, Bogdan; Wilkins, Arjun
Publication Date: 2020
Creators: Messing, Solomon; DeGregorio, Christina; Hillenbrand, Bennett; King, Gary; Mahanti, Saurav; Mukerjee, Zagreb; Nayak, Chaya; Persily, Nate; State, Bogdan; Wilkins, Arjun

This is a codebook for data on the demographics of people who viewed, shared, and otherwise interacted with web pages (URLs) shared on Facebook, between January 1, 2017 and October 31, 2022. The data has about 68 million URLs, over 3.1 trillion rows, and over 71 trillion cell values. It results from a collaboration between Facebook and Social Science One (at IQSS at Harvard), originally prepared for Social Science One grantees and describes the “full” URLs dataset, including its scope, structure, and fields. This is version 10 of the codebook and data (released 4/13/2023), first described by Gary King and Nathaniel Persily at https://socialscience.one/blog/update-social-science-one. The dataset’s structure is organized to facilitate detailed analysis. Each entry corresponds to a unique URL and includes aggregated user interaction metrics. These metrics are further broken down by various demographic dimensions, such as age, gender, and country. For users in the United States, additional categorizations include political page affinity, offering insights into how different political leanings may influence content engagement.

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