References
[1]. Baker EH. Socio-economic Status, Definition. In: The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society. John Wiley & Sons, Ltd; 2014, 2210–4.
[2]. Arrow K, Bowles S, Durlauf SN. Meritocracy and Economic Inequality. Princeton University Press; 2018. 367 p.
[3]. Oakes JM, Kaufman JS. Methods in Social Epidemiology. 2017;603.
[4]. Singh GK, Ghandour RM. Impact of Neighborhood Social Conditions and Household Socioeconomic Status on Behavioral Problems Among US Children. Matern Child Health J. 2012, 16(1):158–69.
[5]. Wang L, He S, Su S, et al. Urban neighborhood socio-economic status (SES) inference: A machine learning approach based on semantic and sentimental analysis of online housing advertisements. Habitat International. 2022, 124:102572.
[6]. Ilic L, Sawada M, Zarzelli A. Deep mapping gentrification in a large Canadian city using deep learning and Google Street View. Ribeiro HV, editor. PLoS ONE. 2019, 14(3):e0212814.
[7]. Zhang G, Guo X, Li D, Jiang B. Evaluating the Potential of LJ1-01 Nighttime Light Data for Modeling Socio-Economic Parameters. Sensors. 2019, 19(6):1465.
[8]. Abitbol JL. Interpretable socio-economic status inference from aerial imagery through urban patterns. 2020;2:12.
[9]. Niu T, Chen Y, Yuan Y. Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou. Sustainable Cities and Society. 2020, 54:102014.
[10]. Sheehan E, Meng C, Tan M, et al. Predicting Economic Development using Geolocated Wikipedia Articles. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage AK USA: ACM; 2019. 2698–706.
[11]. Suel E, Bhatt S, Brauer M, Flaxman S, Ezzati M. Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas. Remote Sensing of Environment. 2021, 257:112339.
[12]. Bourdieu P. Distinction a Social Critique of the Judgement of Taste. In: Inequality Classic Readings in Race, Class, and Gender. Routledge; 2006.
[13]. Hu M, Liu B. Mining and Summarizing Customer Reviews. 10.
[14]. Chen W, Wu X, Miao J. Housing and Subjective Class Identification in Urban China. Chinese Sociological Review. 2019, 51(3):221–50.
[15]. Leslie E, Cerin E, Kremer P. Perceived Neighborhood Environment and Park Use as Mediators of the Effect of Area Socio-Economic Status on Waiking Behaviors. 10.
[16]. Dodson J, Gleeson B, Sipe N. Transport Disadvantage and Social Status: A review of literature and methods. 63.
[17]. Mouw T. Job Relocation and the Racial Gap in Unemployment in Detroit and Chicago, 1980 to 1990. American Sociological Review. 2000;65(5):730–53.
[18]. As Long as There are Neighborhood - John R. Logan, 2016.
Cite this article
Yuan,W. (2023). The estimation of spatial distribution patterns of different socio-economic status (SES) groups by housing advertisement data and machine learning techniques: a case study in brooklyn, new york. Applied and Computational Engineering,6,1316-1328.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Baker EH. Socio-economic Status, Definition. In: The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society. John Wiley & Sons, Ltd; 2014, 2210–4.
[2]. Arrow K, Bowles S, Durlauf SN. Meritocracy and Economic Inequality. Princeton University Press; 2018. 367 p.
[3]. Oakes JM, Kaufman JS. Methods in Social Epidemiology. 2017;603.
[4]. Singh GK, Ghandour RM. Impact of Neighborhood Social Conditions and Household Socioeconomic Status on Behavioral Problems Among US Children. Matern Child Health J. 2012, 16(1):158–69.
[5]. Wang L, He S, Su S, et al. Urban neighborhood socio-economic status (SES) inference: A machine learning approach based on semantic and sentimental analysis of online housing advertisements. Habitat International. 2022, 124:102572.
[6]. Ilic L, Sawada M, Zarzelli A. Deep mapping gentrification in a large Canadian city using deep learning and Google Street View. Ribeiro HV, editor. PLoS ONE. 2019, 14(3):e0212814.
[7]. Zhang G, Guo X, Li D, Jiang B. Evaluating the Potential of LJ1-01 Nighttime Light Data for Modeling Socio-Economic Parameters. Sensors. 2019, 19(6):1465.
[8]. Abitbol JL. Interpretable socio-economic status inference from aerial imagery through urban patterns. 2020;2:12.
[9]. Niu T, Chen Y, Yuan Y. Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou. Sustainable Cities and Society. 2020, 54:102014.
[10]. Sheehan E, Meng C, Tan M, et al. Predicting Economic Development using Geolocated Wikipedia Articles. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage AK USA: ACM; 2019. 2698–706.
[11]. Suel E, Bhatt S, Brauer M, Flaxman S, Ezzati M. Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas. Remote Sensing of Environment. 2021, 257:112339.
[12]. Bourdieu P. Distinction a Social Critique of the Judgement of Taste. In: Inequality Classic Readings in Race, Class, and Gender. Routledge; 2006.
[13]. Hu M, Liu B. Mining and Summarizing Customer Reviews. 10.
[14]. Chen W, Wu X, Miao J. Housing and Subjective Class Identification in Urban China. Chinese Sociological Review. 2019, 51(3):221–50.
[15]. Leslie E, Cerin E, Kremer P. Perceived Neighborhood Environment and Park Use as Mediators of the Effect of Area Socio-Economic Status on Waiking Behaviors. 10.
[16]. Dodson J, Gleeson B, Sipe N. Transport Disadvantage and Social Status: A review of literature and methods. 63.
[17]. Mouw T. Job Relocation and the Racial Gap in Unemployment in Detroit and Chicago, 1980 to 1990. American Sociological Review. 2000;65(5):730–53.
[18]. As Long as There are Neighborhood - John R. Logan, 2016.