osmBias: Validating OpenStreetMap Point of Interest Data for Individual-Level Survey-Based Analysis

Daria Dementeva; Anne-Kathrin Stroppe

osmBias: Validating OpenStreetMap Point of Interest Data for Individual-Level Survey-Based Analysis

Daria Dementeva
, Anne-Kathrin Stroppe

Urban Development & Mobility

Research output: Other contribution

Abstract

The growing availability of big geodata allows survey researchers to enrich survey datasets with innovative geographic context measures. This development opens up new research opportunities and strengthens spatial perspectives within established theoretical frameworks. Against this backdrop, the use of crowdsourced geospatial data, such as OpenStreetMap (OSM), has increased substantially in survey-based social science research. However, linking OSM data to survey responses raises important questions about data quality and fitness for use, as the database relies on volunteer contributions and lacks standardized quality assurance procedures. At the same time, comparable official data sources are often unavailable, outdated, costly, or insufficiently detailed for many research applications. OSM thus emerges as a practical and cost-effective alternative. Nevertheless, systematic assessments of OSM data quality remain essential as variations in OSM coverage and accuracy may affect substantive research findings.

This tool fills this critical void and serves as an innovative hands-on primer on OSM data quality checks in survey-based analyses. Specifically, it focuses on assessing the completeness and coverage of OSM POI data when used to objectively proxy local public service provision in survey-based research.

Original language	English
Type	Data and Software
Media of output	Web
Publication status	Published - 1 Apr 2026

Bibliographical note

Dementeva, Daria, and Anne Stroppe. 2026. “osmBias - Validating OpenStreetMap Point of Interest Data for Individual-Level Survey-Based Analysis.” In F. Kraemer, Y. Peters, A.-K. Stroppe, J. Daikeler, F. Draxler, F. Kreuter, F. Keusch, T. Knopf, L. Mejia Lopez, B. Rammstedt, P. Siegers, H. Silber, J. Sun, C. Wagner, K. Weller, C. Yıldız, S. Jünger, S. Kapidzic, & L. Young (Eds.), KODAQS Toolbox. https://kodaqs-toolbox.gesis.org/github.com/dariadementeva/KODAQS_OSM.

Access to Document

Cite this

@misc{e4588c63789a4685bed46b4c2dffaa82,

title = "osmBias: Validating OpenStreetMap Point of Interest Data for Individual-Level Survey-Based Analysis",

abstract = "The growing availability of big geodata allows survey researchers to enrich survey datasets with innovative geographic context measures. This development opens up new research opportunities and strengthens spatial perspectives within established theoretical frameworks. Against this backdrop, the use of crowdsourced geospatial data, such as OpenStreetMap (OSM), has increased substantially in survey-based social science research. However, linking OSM data to survey responses raises important questions about data quality and fitness for use, as the database relies on volunteer contributions and lacks standardized quality assurance procedures. At the same time, comparable official data sources are often unavailable, outdated, costly, or insufficiently detailed for many research applications. OSM thus emerges as a practical and cost-effective alternative. Nevertheless, systematic assessments of OSM data quality remain essential as variations in OSM coverage and accuracy may affect substantive research findings. This tool fills this critical void and serves as an innovative hands-on primer on OSM data quality checks in survey-based analyses. Specifically, it focuses on assessing the completeness and coverage of OSM POI data when used to objectively proxy local public service provision in survey-based research. ",

author = "Daria Dementeva and Anne-Kathrin Stroppe",

note = "Dementeva, Daria, and Anne Stroppe. 2026. “osmBias - Validating OpenStreetMap Point of Interest Data for Individual-Level Survey-Based Analysis.” In F. Kraemer, Y. Peters, A.-K. Stroppe, J. Daikeler, F. Draxler, F. Kreuter, F. Keusch, T. Knopf, L. Mejia Lopez, B. Rammstedt, P. Siegers, H. Silber, J. Sun, C. Wagner, K. Weller, C. Yıldız, S. J{\"u}nger, S. Kapidzic, \& L. Young (Eds.), KODAQS Toolbox. https://kodaqs-toolbox.gesis.org/github.com/dariadementeva/KODAQS\_OSM.",

year = "2026",

month = apr,

day = "1",

language = "English",

type = "Other",

}

TY - GEN

T1 - osmBias: Validating OpenStreetMap Point of Interest Data for Individual-Level Survey-Based Analysis

AU - Dementeva, Daria

AU - Stroppe, Anne-Kathrin

N1 - Dementeva, Daria, and Anne Stroppe. 2026. “osmBias - Validating OpenStreetMap Point of Interest Data for Individual-Level Survey-Based Analysis.” In F. Kraemer, Y. Peters, A.-K. Stroppe, J. Daikeler, F. Draxler, F. Kreuter, F. Keusch, T. Knopf, L. Mejia Lopez, B. Rammstedt, P. Siegers, H. Silber, J. Sun, C. Wagner, K. Weller, C. Yıldız, S. Jünger, S. Kapidzic, & L. Young (Eds.), KODAQS Toolbox. https://kodaqs-toolbox.gesis.org/github.com/dariadementeva/KODAQS_OSM.

PY - 2026/4/1

Y1 - 2026/4/1

N2 - The growing availability of big geodata allows survey researchers to enrich survey datasets with innovative geographic context measures. This development opens up new research opportunities and strengthens spatial perspectives within established theoretical frameworks. Against this backdrop, the use of crowdsourced geospatial data, such as OpenStreetMap (OSM), has increased substantially in survey-based social science research. However, linking OSM data to survey responses raises important questions about data quality and fitness for use, as the database relies on volunteer contributions and lacks standardized quality assurance procedures. At the same time, comparable official data sources are often unavailable, outdated, costly, or insufficiently detailed for many research applications. OSM thus emerges as a practical and cost-effective alternative. Nevertheless, systematic assessments of OSM data quality remain essential as variations in OSM coverage and accuracy may affect substantive research findings. This tool fills this critical void and serves as an innovative hands-on primer on OSM data quality checks in survey-based analyses. Specifically, it focuses on assessing the completeness and coverage of OSM POI data when used to objectively proxy local public service provision in survey-based research.

AB - The growing availability of big geodata allows survey researchers to enrich survey datasets with innovative geographic context measures. This development opens up new research opportunities and strengthens spatial perspectives within established theoretical frameworks. Against this backdrop, the use of crowdsourced geospatial data, such as OpenStreetMap (OSM), has increased substantially in survey-based social science research. However, linking OSM data to survey responses raises important questions about data quality and fitness for use, as the database relies on volunteer contributions and lacks standardized quality assurance procedures. At the same time, comparable official data sources are often unavailable, outdated, costly, or insufficiently detailed for many research applications. OSM thus emerges as a practical and cost-effective alternative. Nevertheless, systematic assessments of OSM data quality remain essential as variations in OSM coverage and accuracy may affect substantive research findings. This tool fills this critical void and serves as an innovative hands-on primer on OSM data quality checks in survey-based analyses. Specifically, it focuses on assessing the completeness and coverage of OSM POI data when used to objectively proxy local public service provision in survey-based research.

M3 - Other contribution

ER -