In the era of data revolution, new types of data and data sources allow researchers to find innovative ways to study society and its dynamics. The concept of neighbourhood effects (NE) was born within the sociological debate on the relationship between territory and social phenomena inaugurated by Durkheim and continued by other authors from many disciplines. NE is a particular concept that is borne more by empirical evidence than theory. This aspect is quite problematic because it is easy to find studies that investigate how spatial characteristics influence social phenomena; however, there is no agreement on the ways in which spatial influence manifests itself or on which spatial elements have a sociological bearing. Initially absent in Internet studies, NE have been progressively investigated owing to the geo-media. Crowdsourced Geographic Information has made it possible to jointly analyze two dimensions previously considered incompatible, such as the online and offline worlds. The purpose of this study is twofold. The first objective is to analyze how the discourse on NE is evolving in digital studies that use crowdsourced Geographic Information. The second objective is to identify the critical elements of this approach. In particular, we will try to give the reader the answer to the following questions: What kind of geo-media has been used? Which topics do NE research focus on? What are the hypothesized mechanisms that link space and social phenomena? What are the most frequently used approaches for this purpose? A systematic literature review was used to answer these questions.
| Published in | Social Sciences (Volume 12, Issue 2) |
| DOI | 10.11648/j.ss.20231202.13 |
| Page(s) | 64-75 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Neighbourhood Effects, Crowdsourced Geographic Information, Social Media, Ecological Analysis, Literature Review
| [1] | Adelfio, M., Serrano-Estrada, L., Martí-Ciriquián, P., Kain, J. H. & Stenberg, J. (2020). Social activity in Gothenburg’s intermediate city: Mapping third places through social media data. Applied Spatial Analysis and Policy. 13, 985-1017. doi: 10.1007/s12061-020-09338-3. |
| [2] | Alagha, I. (2021). Topic modeling and sentiment analysis of Twitter discussions on COVID-19 from spatial and temporal perspectives. Journal of Information Science Theory and Practice. 9, 35-53. doi: 10.1633/JISTaP.2021.9.1.3. |
| [3] | Ballatore, A. & De Sabbata, S. (2020). Los Angeles as a digital place: The geographies of user-generated content. Transactions in GIS. 24, 880-902. doi: 10.1111/tgis.12600. |
| [4] | Calabrese, F., Dahlem, D., Gerber, A., Paul, D., Chen, X., Rowland, J. & Ratti, C. (2011, October). The connected states of america: Quantifying social radii of influence. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing (pp. 223-230). IEEE. |
| [5] | Catalano, R. & Pickett, K. E. (2003). “A Taxonomy of Research Concerned with place and health”, in The handbook of social studies in health and medicine, ed. Albrecht G. L., Fitzpatrick R., Scrimshaw S. C. (Thousand Oaks, CA: Sage Publication), 64-83. |
| [6] | Cavan, R. S. (1983). The Chicago school of sociology, 1918-1933. Urban life. 11, 407-420. doi: 10.1177/0098303983011004003. |
| [7] | Cooper H. (1998) Synthesizing Research: A Guide for Literature Reviews, 3rd edn. Sage Publications, Thousand Oaks, CA. |
| [8] | Cox, K. R. (1969). The voting decision in a spatial context. Progress in geography. 1, 81-117. |
| [9] | Cutchin, M. P., Eschbach, K., Mair, C. A., Ju, H. & Goodwin, J. S. (2011). The socio-spatial neighborhood estimation method: an approach to operationalizing the neighborhood concept. Health & place. 17, 1113-1121. doi: 10.1016/j.healthplace.2011.05.011. |
| [10] | Davis, J. A. (1961). Compositional effects, role systems, and the survival of small discussion groups. Public Opinion Quarterly. 25, 574-584. doi: 10.1086/267053. |
| [11] | De Falco, A., De Falco, C. C. & Ferracci, M. (2022). Geo-Social Media and Socio-Territorial Distribution: A Study on the Italian Case. Italian Sociological Review. 12, 685-708. doi: 10.13136/isr.v12i7S.577. |
| [12] | De Falco, C. C., Punziano, G. & Trezza, D. (2021). A mixed content analysis design in the study of the Italian perception of COVID-19 on Twitter. Athens Journal of Social Sciences. 8, 191-210. doi: 10.30958/ajss.8-3-3. |
| [13] | de Vos, S. (1998). The analysis of compositional effects as exemplified by the study of elections. GeoJournal. 44, 43–49. doi: 10.1023/A:1006820202876. |
| [14] | Emani, C. K., Cullot, N. & Nicolle, C. (2015). Understandable big data: a survey. Computer science review. 17, 70-81. |
| [15] | Freelon, D. (2018). Computational research in the post-API age. Political Communication. 35, 665-668. doi: 10.1080/10584609.2018.1477506. |
| [16] | Galster, G. C. (2012). “The mechanism (s) of neighbourhood effects: Theory, evidence, and policy implications”, in Neighbourhood effects research: New perspectives, (Dordrecht: Springer), 23-56. |
| [17] | Ghosh, D. & Guha, R. (2013). What are we ‘tweeting’about obesity? Mapping tweets with topic modeling and Geographic Information System. Cartography and geographic information science. 40, 90-102. doi: 10.1080/15230406.2013.776210. |
| [18] | Gibbons, J. (2022). Why can’t we be friends? The underlying hierarchy of social connections between diverse neighborhoods. Applied Spatial Analysis and Policy. 15, 1339–1365. doi: 10.1007/s12061-022-09453-3. |
| [19] | Gibbons, J., Nara, A. & Appleyard, B. (2018). Exploring the imprint of social media networks on neighborhood community through the lens of gentrification. Environment and Planning B: Urban Analytics and City Science. 45, 470-488. doi: 10.1177/2399808317728289. |
| [20] | Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal. 69, 211-221. doi: 10.1007/s10708-007-9111-y. |
| [21] | Haffner, M. (2018). A spatial analysis of non-English Twitter activity in Houston, TX. Transactions in GIS. 22, 913-929. doi: 10.1111/tgis.12335. |
| [22] | Hootsuite (2022). We are social - Digital 2022 Global Overview Report. |
| [23] | Hu, L., Li, Z. & Ye, X. (2020). Delineating and modeling activity space using geotagged social media data. Cartography and Geographic Information Science. 47, 277-288. doi: 10.1080/15230406.2019.1705187. |
| [24] | Iranmanesh, A. & Atun, R. A. (2018). Exploring the spatial distribution of geo-tagged Twitter feeds via street-centrality measures. Urban Design International. 23, 293-306. doi: 10.1057/s41289-018-0073-0. |
| [25] | Jacobs, J. (1961). The death and life of great American cities (reprint). New York: Random House. |
| [26] | Jargowsky, P. A. (2005). The ecological fallacy. Encyclopedia of social measurement. 1, 574-584. |
| [27] | Jiang, Y., Li, Z. & Ye, X. (2019). Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level. Cartography and geographic information science. 46, 228-242. doi: 10.1080/15230406.2018.1434834. |
| [28] | Johnston, R. (1979). Class conflict and electoral geography. Antipode. 11 (3), 36-43. |
| [29] | Karayazi, S. S., Dane, G. & Vries, B. D. (2021). Utilizing urban geospatial data to Understand heritage attractiveness in Amsterdam. ISPRS International Journal of Geo-Information. 10, 198. doi: 10.3390/ijgi10040198. |
| [30] | King, G., Tanner, M. A. & Rosen, O. (2004). Ecological inference: New methodological strategies. Cambridge: Cambridge University Press. |
| [31] | Knies, G., Melo, P. C. & Zhang, M. (2021). Neighbourhood deprivation, life satisfaction and earnings: Comparative analyses of neighbourhood effects at bespoke scales. Urban Studies. 58, 2640-2659. doi: 10.1177/0042098020956930. |
| [32] | Lang, W., Lang, H., Hui, E. C., Chen, T., Wu, J. & Jahre, M. (2022). Measuring urban vibrancy of neighborhood performance using social media data in Oslo, Norway. Cities. 131, 103908. doi: 10.1016/j.cities.2022.103908. |
| [33] | Legeby, A., Koch, D., Duarte, F., Heine, C., Benson, T., Fugiglando, U. & Ratti, C. (2022). New urban habits in Stockholm following COVID-19. Urban Studies. https://doi.org/10.1177/00420980211070677 |
| [34] | Li, Y., Xie, J., Gao, X. & Law, A. (2021). A Method of selecting potential development regions based on GPS and social network models–from the perspective of tourist behavior. Asia Pacific Journal of Tourism Research. 26, 183-199. doi: 10.1080/10941665.2018.1515092. |
| [35] | Liebst, L. S. Ä. (2016). Reassembling Durkheimian sociology of space. In Spatial Cultures (pp. 246-256). Routledge. |
| [36] | Lupton, D. (2014). Digital sociology. London: Routledge. |
| [37] | Macintyre, S., Maciver, S. & Sooman, A. (1993). Area, class and health: should we be focusing on places or people? Journal of social policy. 22, 213-234. doi: 10.1017/S0047279400019310. |
| [38] | Manovich, L. (2011). Trending: The Promises and the Challenges of Big Social Data. In M. K. Gold (Ed.), Debates in the Digital Humanities. Minneapolis, MN: The University of Minnesota Press. http://manovich.net/index.php/projects/trending-the-promises-and-the-challenges-of-big-social-data |
| [39] | Mejias, U. A. & Couldry, N. (2019). Datafication. Internet Policy Review. 8, 261-278. doi: 10.14763/2019.4.1428. |
| [40] | Mela, A. (2006). Sociologia delle città. Roma: Carocci. |
| [41] | Menzel, H. (1950). Comment on Robinson’s "ecological correlations and the behavior of individuals". American Sociological Review. 15, 674-674. |
| [42] | Middleton, S. E., Kordopatis-Zilos, G., Papadopoulos, S. & Kompatsiaris, Y. (2018). Location extraction from social media: Geoparsing, location disambiguation, and geotagging. ACM Transactions on Information Systems (TOIS). 36, 1-27. doi: 10.1145/3202662. |
| [43] | Miller, W. L. (1977). Electoral dynamics in Britain since 1918. London: Palgrave Macmillan. |
| [44] | Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine. 151 (4), 264-269. |
| [45] | Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews. 4 (1), 1-9. |
| [46] | Niu, H. & Silva, E. A. (2020). Crowdsourced data mining for urban activity: Review of data sources, applications, and methods. Journal of Urban Planning and Development. 146, 04020007. doi: 10.1061/(ASCE)UP.1943-5444.0000566. |
| [47] | Norris, M. & Oppenheim, C. (2007). Comparing alternatives to the Web of Science for coverage of the social sciences’ literature. Journal of informetrics. 1 (2), 161-169. |
| [48] | Oakes, J. M., Andrade, K. E., Biyoow, I. M. & Cowan, L. T. (2015). Twenty years of neighborhood effect research: an assessment. Current epidemiology reports. 2, 80-87. doi: 10.1007/s40471-015-0035-7. |
| [49] | Ristea, A., Al Boni, M., Resch, B., Gerber, M. S. & Leitner, M. (2020). Spatial crime distribution and prediction for sporting events using social media. International Journal of Geographical Information Science. 34, 1708-1739. doi: 10.1080/13658816.2020.1719495. |
| [50] | Sampson, R. J., Morenoff, J. D. & Gannon-Rowley, T. (2002). Assessing "neighborhood effects": Social processes and new directions in research. Annual review of sociology. 177-187. Doi: https://doi.org/10.1146/annurev.soc.28.110601.141114. |
| [51] | Settersten, R. A. (2001). “Community Organization and the Life Course”, in International Encyclopedia of the Social & Behavioral Sciences, ed. N. J. Smelser, P. B. Baltes (Oxford, UK: Pergamon Press), 2367-2371. |
| [52] | Stefanidis, A., Crooks, A. & Radzikowski, J. (2013). Harvesting ambient geospatial information from social media feeds. GeoJournal. 78: 2, 319–338. Doi: https://doi.org/10.1007/s10708-011-9438-2. |
| [53] | Stein, R. E. (2014). Neighborhood scale and collective efficacy: Does size matter? Sociology Compass. 8: 2, 119-128. https://doi.org/10.1111/soc4.12127 |
| [54] | Strassoldo, R. (1990). TheSociology of Space, Culture-Space-History: Proceedings 11th International Conference of the IAPS, Ankara, Turkey. |
| [55] | Thielmann, T. (2010). Locative media and mediated localities. Aether: the journal of media geography. 5, 1-17. |
| [56] | Tingsten, H. (1937). Political Behavior; Studies in Election Statistics. London: PS King and Son. |
| [57] | Tranfield, D., Denyer, D. & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British journal of management. 14 (3), 207-222. |
| [58] | Tu, W., Zhu, T., Xia, J., Zhou, Y., Lai, Y., Jiang, J. & Li, Q. (2020). Portraying the spatial dynamics of urban vibrancy using multisource urban big data. Computers, Environment and Urban Systems. 80, 101428. Doi: 10.1016/j.compenvurbsys.2019.101428. |
| [59] | Väisänen, T., Järv, O., Toivonen, T. & Hiippala, T. (2022). Mapping urban linguistic diversity with social media and population register data. Computers, Environment and Urban Systems. 97, 101857. Doi: https://doi.org/10.1016/j.compenvurbsys.2022.101857. |
| [60] | Valls, F. & Roca, J. (2021). Visualizing digital traces for sustainable urban management: mapping tourism activity on the virtual public space. Sustainability. 13: 6, 3159. Doi: https://doi.org/10.3390/su13063159. |
| [61] | van Ham, M. & Manley, D. (2012). Neighbourhood Effects Research at a Crossroads. Ten Challenges for Future Research Introduction. Environment and Planning A. 44: 12, 2787-2793 Doi: https://doi.org/10.1068/a45439. |
| [62] | Wang, Z., Ye, X., Lee, J., Chang, X., Liu, H. & Li, Q. (2018). A spatial econometric modeling of online social interactions using microblogs. Computers, Environment and Urban Systems. 70, 53-58. Doi: https://doi.org/10.1016/j.compenvurbsys.2018.02.001. |
| [63] | Wilson W.J. (1987). The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy. Chicago: University of Chicago Press. |
| [64] | Yin, F., Crooks, A. & Yin, L. (2022). Information propagation on cyber, relational and physical spaces about COVID-19 vaccine: Using social media and splatial framework. Computers, Environment and Urban Systems. 98, 101887. Doi: 10.1016/j.compenvurbsys.2022.101887. |
| [65] | Yin, J., Soliman, A., Yin, D. & Wang, S. (2017). Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data. International Journal of Geographical Information Science. 31: 7, 1293-1313. Doi: https://doi.org/10.1080/13658816.2017.1282615. |
| [66] | Zola, P., Cortez, P. & Carpita, M. (2019). Twitter user geolocation using web country noun searches. Decision Support Systems. 120, 50-59. Doi: https://doi.org/10.1016/j.dss.2019.03.006. |
APA Style
Ciro Clemente De Falco. (2023). Geo-Media and Neighbourhood Effects’ Studies: A New Frontier. Social Sciences, 12(2), 64-75. https://doi.org/10.11648/j.ss.20231202.13
ACS Style
Ciro Clemente De Falco. Geo-Media and Neighbourhood Effects’ Studies: A New Frontier. Soc. Sci. 2023, 12(2), 64-75. doi: 10.11648/j.ss.20231202.13
AMA Style
Ciro Clemente De Falco. Geo-Media and Neighbourhood Effects’ Studies: A New Frontier. Soc Sci. 2023;12(2):64-75. doi: 10.11648/j.ss.20231202.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ss.20231202.13,
author = {Ciro Clemente De Falco},
title = {Geo-Media and Neighbourhood Effects’ Studies: A New Frontier},
journal = {Social Sciences},
volume = {12},
number = {2},
pages = {64-75},
doi = {10.11648/j.ss.20231202.13},
url = {https://doi.org/10.11648/j.ss.20231202.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20231202.13},
abstract = {In the era of data revolution, new types of data and data sources allow researchers to find innovative ways to study society and its dynamics. The concept of neighbourhood effects (NE) was born within the sociological debate on the relationship between territory and social phenomena inaugurated by Durkheim and continued by other authors from many disciplines. NE is a particular concept that is borne more by empirical evidence than theory. This aspect is quite problematic because it is easy to find studies that investigate how spatial characteristics influence social phenomena; however, there is no agreement on the ways in which spatial influence manifests itself or on which spatial elements have a sociological bearing. Initially absent in Internet studies, NE have been progressively investigated owing to the geo-media. Crowdsourced Geographic Information has made it possible to jointly analyze two dimensions previously considered incompatible, such as the online and offline worlds. The purpose of this study is twofold. The first objective is to analyze how the discourse on NE is evolving in digital studies that use crowdsourced Geographic Information. The second objective is to identify the critical elements of this approach. In particular, we will try to give the reader the answer to the following questions: What kind of geo-media has been used? Which topics do NE research focus on? What are the hypothesized mechanisms that link space and social phenomena? What are the most frequently used approaches for this purpose? A systematic literature review was used to answer these questions.},
year = {2023}
}
TY - JOUR T1 - Geo-Media and Neighbourhood Effects’ Studies: A New Frontier AU - Ciro Clemente De Falco Y1 - 2023/03/28 PY - 2023 N1 - https://doi.org/10.11648/j.ss.20231202.13 DO - 10.11648/j.ss.20231202.13 T2 - Social Sciences JF - Social Sciences JO - Social Sciences SP - 64 EP - 75 PB - Science Publishing Group SN - 2326-988X UR - https://doi.org/10.11648/j.ss.20231202.13 AB - In the era of data revolution, new types of data and data sources allow researchers to find innovative ways to study society and its dynamics. The concept of neighbourhood effects (NE) was born within the sociological debate on the relationship between territory and social phenomena inaugurated by Durkheim and continued by other authors from many disciplines. NE is a particular concept that is borne more by empirical evidence than theory. This aspect is quite problematic because it is easy to find studies that investigate how spatial characteristics influence social phenomena; however, there is no agreement on the ways in which spatial influence manifests itself or on which spatial elements have a sociological bearing. Initially absent in Internet studies, NE have been progressively investigated owing to the geo-media. Crowdsourced Geographic Information has made it possible to jointly analyze two dimensions previously considered incompatible, such as the online and offline worlds. The purpose of this study is twofold. The first objective is to analyze how the discourse on NE is evolving in digital studies that use crowdsourced Geographic Information. The second objective is to identify the critical elements of this approach. In particular, we will try to give the reader the answer to the following questions: What kind of geo-media has been used? Which topics do NE research focus on? What are the hypothesized mechanisms that link space and social phenomena? What are the most frequently used approaches for this purpose? A systematic literature review was used to answer these questions. VL - 12 IS - 2 ER -
Email: