References
- 1Abadie, A, Athey, S, Imbens, GW and Wooldridge, J. 2017. When should you adjust standard errors for clustering? (No. w24003). National Bureau of Economic Research. DOI: 10.3386/w24003
- 2Babisch, W. 2014. Updated exposure-response relationship between road traffic noise and coronary heart diseases: a meta-analysis. Noise and Health, 16(68): 1. DOI: 10.4103/1463-1741.127847
- 3Bačić, D and Fadlalla, A. 2016. Business information visualization intellectual contributions: An integrative framework of visualization capabilities and dimensions of visual intelligence. Decision Support Systems, 89: 77–86. DOI: 10.1016/j.dss.2016.06.011
- 4Banzhaf, HS, Ma, L and Timmins, C. 2019. Environmental Justice: Establishing Causal Relationships. Annual Review of Resource Economics, 11(1): 377–398. DOI: 10.1146/annurev-resource-100518-094131
- 5Best, H and Rüttenauer, T. 2018. How Selective Migration Shapes Environmental Inequality in Germany: Evidence from Micro-level Panel Data. European Sociological Review, 34(1): 52–63. DOI: 10.1093/esr/jcx082
- 6Bluemke, M, Resch, B, Lechner, C, et al. 2017. Integrating Geographic Information into Survey Research: Current Applications, Challenges and Future Avenues. Survey Research Methods, 11(3): 307–327. DOI: 10.18148/srm/2017.v11i3.6733
- 7Bocquier, A, Cortaredona, S, Boutin, C, David, A, Bigot, A, Chaix, B, Verger, P, et al. 2012. Small-area analysis of social inequalities in residential exposure to road traffic noise in Marseilles, France. The European Journal of Public Health, 23(4): 540–546. DOI: 10.1093/eurpub/cks059
- 8Bosch, T, Cyganiak, R, Wackerow, J and Zapilko, B. 2012, September. Leveraging the DDI model for linked statistical data in the social, behavioural, and economic sciences. In International Conference on Dublin Core and Metadata Applications, 46–55.
- 9Bosch, T, Cyganiak, R, Wackerow, J and Zapilko, B. 2015. DDI-RDF Discovery Vocabulary. Retrieved April 2, 2019, from
http://rdf-vocabulary.ddialliance.org/discovery.html . - 10Bosnjak, M, Dannwolf, T, Enderle, T, Schaurer, I, Struminskaya, B, Tanner, A and Weyandt, KW. 2018. Establishing an open probability-based mixed-mode panel of the general population in Germany: The GESIS panel. Social Science Computer Review, 36(1): 103–115. DOI: 10.1177/0894439317697949
- 11Crowder, K and Downey, L. 2010. Interneighborhood migration, race, and environmental hazards: Modeling microlevel processes of environmental inequality. American Journal of Sociology, 115(4): 1110–1149. DOI: 10.1086/649576
- 12DDI Alliance. 2018. Document, Discover and Interoperate. Retrieved April 2, 2019, from
https://www.ddialliance.org/ . DOI: 10.1111/jomf.12355 - 13Downey, L, Crowder, K and Kemp, RJ. 2017. Family structure, residential mobility, and environmental inequality. Journal of Marriage and Family, 79(2): 535–555. DOI: 10.1111/jomf.12355
- 14Dreger, S, Schüle, S, Hilz, L, et al. 2019. Social Inequalities in Environmental Noise Exposure: A Review of Evidence in the WHO European Region. International Journal of Environmental Research and Public Health, 16(6): 1011. DOI: 10.3390/ijerph16061011
- 15ECMA. 2017. Standard ECMA-404 The JSON Data Interchange Syntax. Retrieved April 2, 2019, from
https://www.ecma-international.org/publications/standards/Ecma-404.htm . - 16Edwards, PN, Mayernik, MS, Batcheller, AL, et al. 2011. Science friction: Data, metadata, and collaboration. Social Studies of Science, 41(5): 667–690. DOI: 10.1177/0306312711413314
- 17European Parliament, Council of the European Union. 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Retrieved April 2, 2019, from
http://data.europa.eu/eli/reg/2016/679/oj . - 18Fielding, RT and Taylor, RN. 2000. Architectural styles and the design of network-based software architectures (Vol. 7). Doctoral dissertation: University of California, Irvine.
- 19Förster, A. 2018. Ethnic heterogeneity and electoral turnout: Evidence from linking neighbourhood data with individual voter data. Electoral Studies, 53: 57–65. DOI: 10.1016/j.electstud.2018.03.002
- 20GESIS. 2017. GESIS Panel – Extended Edition. GESIS Data Archive, Cologne, ZA5664 Datenfile Version 19.0.0. DOI: 10.4232/1.12742
- 21Glatter-Götz, H, Mohai, P, Haas, W, et al. n.d. Environmental inequality in Austria: do inhabitants’ socioeconomic characteristics differ depending on their proximity to industrial polluters? Environmental Research Letters. DOI: 10.1088/1748-9326/ab1611
- 22Goebel, J. 2017. SOEP 2015 -Informationen zu den SOEP-Geocodes in SOEP v32. DIW.
- 23Goodchild, M, Haining, R and Wise, S. 1992. “Integrating GIS and spatial data analysis: problems and possibilities.” International journal of geographical information systems, 65: 407–423. DOI: 10.1080/02693799208901923
- 24Goodchild, MF. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4): 211–221. DOI: 10.1007/s10708-007-9111-y
- 25Haklay, M. 2010. How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. Environment and Planning B: Planning and Design, 37(4): 682–703. DOI: 10.1068/b35097
- 26Ilieva, RT and McPhearson, T. 2018. Social-media data for urban sustainability. Nature Sustainability, 1(10): 553–565. DOI: 10.1038/s41893-018-0153-6
- 27Jünger, S. 2019.
Using Georeferenced Data in Social Science Survey Research. The Method of Spatial Linking and Its Application with the German General Social Survey and the GESIS Panel . GESIS-Schriftenreihe 24. Köln: GESIS – Leibniz-Institut für Sozialwissenschaften. DOI: 10.21241/ssoar.63688. - 28Kabisch, N and Haase, D. 2014. Green justice or just green? Provision of urban green spaces in Berlin, Germany. Landscape and Urban Planning, 122: 129–139. DOI: 10.1016/j.landurbplan.2013.11.016
- 29Keenan, PB and Jankowski, P. 2019. Spatial Decision Support Systems: Three decades on. Decision Support Systems, 116: 64–76. DOI: 10.1016/j.dss.2018.10.010
- 30Klinger, J, Müller, S and Schaeffer, M. 2017. Der Halo-Effekt in einheimisch-homogenen Nachbarschaften. Zeitschrift für Soziologie, 46(6): 402–419. DOI: 10.1515/zfsoz-2017-1022
- 31Krüger, T, Meinel, G and Schumacher, U. 2013. Land-use monitoring by topographic data analysis. Cartography and Geographic Information Science, 40(3): 220–228. DOI: 10.1080/15230406.2013.809232
- 32Li, J, Auchincloss, AH, Rodriguez, DA, et al. 2020. Determinants of Residential Preferences Related to Built and Social Environments and Concordance between Neighborhood Characteristics and Preferences. Journal of Urban Health, 97(1): 62–77. DOI: 10.1007/s11524-019-00397-7
- 33Marques, S and Lima, ML. 2011. Living in grey areas: Industrial activity and psychological health. Journal of Environmental Psychology, 31(4): 314–322. DOI: 10.1016/j.jenvp.2010.12.002
- 34Mayer, A and Smith, EK. 2019. Unstoppable climate change? The influence of fatalistic beliefs about climate change on behavioural change and willingness to pay cross-nationally. Climate Policy, 19(4): 511–523. DOI: 10.1080/14693062.2018.1532872
- 35McAvay, H. 2019. Socioeconomic status and long-term exposure to disadvantaged neighbourhoods in France. Urban Studies. DOI: 10.1177/0042098019882338
- 36Miller, S. 2019. Park Access and Equity in a Segregated, Southern U.S. City: A Case Study of Tallahassee, FL. Environmental Justice, 12(3): 85–91. DOI: 10.1089/env.2018.0026
- 37Mueller, M and Pross, B. 2014. OGC® WPS 2.0 Interface Standard. Retrieved April 2, 2019, from
http://docs.opengeospatial.org/is/14-065/14-065.html . DOI: 10.1177/0042098009104575 - 38Müller, M and Portele, C. 2005.
GDI-Architekturmodell . In Bernhard, L and Fritzke, J (eds.), Geodateninfrastruktur: Grundlagen und Anwendung, 83–92. Heidelberg: Herbert Wichmann Verlag. - 39Müller, S. 2019.
Räumliche Verknüpfung georeferenzierter Umfragedaten mit Geodaten: Chancen, Herausforderungen und praktische Empfehlungen . In: Jensen, U, Netscher, S and Weller, K (eds.), Forschungsdatenmanagement Sozialwissenschaftlicher Umfragedaten, 211–229. Grundlagen Und Praktische Lösungen Für Den Umgang Mit Quantitativen Forschungsdaten. Opladen, Berlin, Toronto: Verlag Barbara Budrich. DOI: 10.2307/j.ctvbkk1p8.15 - 40Oiamo, TH, Baxter, J, Grgicak-Mannion, A, Xu, X and Luginaah, IN. 2015. Place effects on noise annoyance: Cumulative exposures, odour annoyance and noise sensitivity as mediators of environmental context. Atmospheric Environment, 116: 183–193. DOI: 10.1016/j.atmosenv.2015.06.024
- 41Pedersen, E. 2015. City dweller responses to multiple stressors intruding into their homes: Noise, light, odour, and vibration. International journal of environmental research and public health, 12(3): 3246–3263. DOI: 10.3390/ijerph120303246
- 42Preisendörfer, P. 2014. Umweltgerechtigkeit. Von sozial-räumlicher Ungleichheit hin zu postulierter Ungerechtigkeit lokaler Umweltbelastungen. Soziale Welt, 65(1): 25–45. DOI: 10.5771/0038-6073-2014-1-25
- 43Robinson, AC, Demšar, U, Moore, AB, Buckley, A, Jiang, B, Field, K, Sluter, CR, et al. 2017. Geospatial big data and cartography: research challenges and opportunities for making maps that matter. International Journal of Cartography, 3(sup1): 32–60. DOI: 10.1080/23729333.2016.1278151
- 44Roos, LL, Hiebert, B, Manivong, P, Edgerton, J, Walld, R, MacWilliam, L and de Rocquigny, J. 2013. What is most important: Social factors, health selection, and adolescent educational achievement. Social Indicators Research, 110(1): 385–414. DOI: 10.1007/s11205-011-9936-0
- 45Rüttenauer, T. 2019. Bringing urban space back in: A multilevel analysis of environmental inequality in Germany. Urban Studies, 56(12): 2549–2567. DOI: 10.1177/0042098018795786
- 46Schmiedeberg, C. 2015. Regional Data in the German Family Panel (pairfam).
- 47Schönwälder, K and Söhn, J. 2009. Immigrant settlement structures in Germany: General patterns and urban levels of concentration of major groups. Urban Studies, 46(7): 1439–1460. DOI: 10.1177/0042098009104575
- 48Schorcht, M, Krüger, T and Meinel, G. 2016. Measuring Land Take: Usability of National Topographic Databases as Input for Land Use Change Analysis: A Case Study from Germany. ISPRS International Journal of Geo-Information, 5(8): 134. DOI: 10.3390/ijgi5080134
- 49Schreiber, G and Yves, R. 2014. RDF 1.1 Primer. Retrieved April 2, 2019, from
https://www.w3.org/TR/rdf11-primer/ . DOI: 10.1016/j.ecolecon.2014.09.013 - 50Schupp, J, Goebel, J, Kroh, M, Schröder, C, Bartels, C, et al. 2018. Sozio-oekonomisches Panel (SOEP), Daten der Jahre 1984–2016. Version: 33.1. SOEP – Sozio-oekonomisches Panel. Dataset. DOI: 10.5684/soep.v33.1
- 51Schweers, S, Kinder-Kurlanda, K, Müller, S and Siegers, P. 2016. Conceptualizing a spatial data infrastructure for the social sciences: An example from Germany. Journal of Map & Geography Libraries, 12(1): 100–126. DOI: 10.1080/15420353.2015.1100152
- 52Sikder, S, Herold, H, Meinel, G, Lorenzen-Zabel, A and Bill, R. 2019. Blessings of open data and technology: e-learning examples on land use monitoring and e-mobility. Conference Proceedings of the STS Conference Graz 2019. Critical Issues in Science, Technology, and Society Studies,
6–7 May 2019 . Graz:TU Graz , 2019, S. 402–414. DOI: 10.3217/978-3-85125-668-0-22 - 53Skinner, C. 2012. Statistical Disclosure Risk: Separating Potential and Harm. International Statistical Review, 80(3): 349–368. DOI: 10.1111/j.1751-5823.2012.00194.x
- 54Sluiter, R, Tolsma, J and Scheepers, P. 2015. At which geographic scale does ethnic diversity affect intra-neighborhood social capital? Social science research, 54: 80–95. DOI: 10.1016/j.ssresearch.2015.06.015
- 55Sørensen, M, Andersen, ZJ, Nordsborg, RB, Becker, T, Tjønneland, A, Overvad, K and Raaschou-Nielsen, O. 2012. Long-term exposure to road traffic noise and incident diabetes: a cohort study. Environmental health perspectives, 121(2): 217–222. DOI: 10.1289/ehp.1205503
- 56Strobl, C. 2017. Dimensionally extended nine-intersection model (de-9im). Encyclopedia of GIS, 470–476. DOI: 10.1007/978-3-319-17885-1_298
- 57Sygna, K, Aasvang, GM, Aamodt, G, Oftedal, B and Krog, NH. 2014. Road traffic noise, sleep and mental health. Environmental research, 131: 17–24. DOI: 10.1016/j.envres.2014.02.010
- 58Thompson, CW, Roe, J, Aspinall, P, Mitchell, R, Clow, A and Miller, D. 2012. More green space is linked to less stress in deprived communities: Evidence from salivary cortisol patterns. Landscape and urban planning, 105(3): 221–229. DOI: 10.1016/j.landurbplan.2011.12.015
- 59Tumba, AG and Ahmad, A. 2014. Geographic Information System and Spatial Data Infrastructure: A developing societies’ perception. Universal Journal of Geoscience, 2(3): 85–92.
- 60Weßling, K. 2016. The influence of socio-spatial contexts on transitions from school to vocational and academic training in Germany (Doctoral dissertation, Eberhard Karls Universität Tübingen).
- 61Wolch, JR, Byrne, J and Newell, JP. 2014. Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’. Landscape and urban planning, 125: 234–244. DOI: 10.1016/j.landurbplan.2014.01.017
- 62World Health Organization. 2016.
Urban green spaces and health: a review of evidence . Copenhagen, Denmark: World Health Organization. - 63Yan, Y, Schultz, M and Zipf, A. 2019. An exploratory analysis of usability of Flickr tags for land use/land cover attribution. Geo-Spatial Information Science, 22(1): 12–22. DOI: 10.1080/10095020.2018.1560044
- 64Zwickl, K, Ash, M and Boyce, JK. 2014. Regional variation in environmental inequality: Industrial air toxics exposure in US cities. Ecological Economics, 107: 494–509. DOI: 10.1016/j.ecolecon.2014.09.013
