Leveraging machine learning to understand urban change with net construction

Abrantes, 2019, Modelling urban form: A multidimensional typology of urban occupation for spatial analysis, Environment and Planning B: Urban Analytics and City Science, 46, 47 Alberti, 1999, Urban patterns and environmental performance: What do we know?, Journal of Planning Education and Research, 19, 151, 10.1177/0739456X9901900205 Alberti, 2005, The effects of urban patterns on ecosystem function, International Regional Science Review, 28, 168, 10.1177/0160017605275160 Alberti, 2001, Quantifying the urban gradient: Linking urban planning and ecology, 89 Anderson, 2017, Vacant lots: An underexplored resource for ecological and social benefits in cities, Urban Forestry & Urban Greening, 21, 146, 10.1016/j.ufug.2016.11.015 Angel, S., Blei, A., Parent, J., Lamson-Hall, P., & Sanchez, N. G. (2016). Atlas of Urban Expansion: The 2016 Edition, Volume 1: Areas and Densities (2016th ed., Vol. 1). NYU Urban Expansion Program; UN-Habitat; Lincoln Institute of Land Policy. https://www.lincolninst.edu/sites/default/files/pubfiles/atlas-of-urban-expansion-2016-volume-1-full.pdf. Auerbach, 2020, Coupling data science with community crowdsourcing for urban renewal policy analysis: An evaluation of Atlanta’s Anti-Displacement Tax Fund, Environment and Planning B: Urban Analytics and City Science, 47, 1081 Bayer, M. (2020). SQLAlchemy—The Database Toolkit for Python. https://www.sqlalchemy.org/. Bishop, 2006 Boeing, G. (2018). Measuring the Complexity of Urban Form and Design (23(4); URBAN DESIGN International, pp. 281–292). Center for Open Science. doi: 10.1057/s41289-018-0072-1. Boessen, 2018, The built environment, spatial scale, and social networks: Do land uses matter for personal network structure?, Environment and Planning B: Urban Analytics and City Science, 45, 400 Bosch, 2019, PyLandStats: An open-source Pythonic library to compute landscape metrics, PLOS ONE, 14, e0225734, 10.1371/journal.pone.0225734 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 Cao, 2021, The effects of 2D and 3D building morphology on urban environments: A multi-scale analysis in the Beijing metropolitan region, Building and Environment, 192, 107635, 10.1016/j.buildenv.2021.107635 Carmona, 2019, Place value: Place quality and its impact on health, social, economic and environmental outcomes, Journal of Urban Design, 24, 1, 10.1080/13574809.2018.1472523 Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide. SPSS Inc., 76. Charles, S. L. (2011). Suburban Gentrification: Understanding the Determinants of Single-family Residential Redevelopment, A Case Study of the Inner-Ring Suburbs of Chicago, IL, 2000-2010. Joint Center for Housing Studies of Harvard University Cambridge, MA. http://140.247.195.238/sites/jchs.harvard.edu/files/w11-1_charles.pdf. Charles, 2014, The spatio-temporal pattern of housing redevelopment in suburban Chicago, 2000–2010, Urban Studies, 51, 2646, 10.1177/0042098013506045 Chin, 2021, The shifting role of public–private partnerships in vacant property redevelopment, Land Use Policy, 105, 105430, 10.1016/j.landusepol.2021.105430 City of Memphis, & Memphis and Shelby County Division of Planning and Development. (2019). Memphis 3.0: The Comprehensive Plan of the City of Memphis, Tennessee. Clapp, 2006, Defining neighborhood boundaries: Are census tracts obsolete?, Journal of Urban Economics, 59, 259, 10.1016/j.jue.2005.10.003 Clifton, 2008, Quantitative analysis of urban form: A multidisciplinary review, Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 1, 17 Coulton, 2012, Defining Neighborhoods for Research and Policy, Cityscape: A Journal of Policy Development and Research, 14, 231 Coulton, 2001, Mapping Residents’ perceptions of neighborhood boundaries: A methodological note, American Journal of Community Psychology, 29, 371, 10.1023/A:1010303419034 Dye, 2007, Teardowns and land values in the Chicago metropolitan area, Journal of Urban Economics, 61, 45, 10.1016/j.jue.2006.06.003 Ewing, 2015, Compactness versus Sprawl: A review of recent evidence from the United States, Journal of Planning Literature, 30, 413, 10.1177/0885412215595439 Ghosh, 2014, Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion, Applied Geography, 48, 31, 10.1016/j.apgeog.2014.01.003 Glaeser, 2018, Big data and big cities: The promises and limitations of improved measures of urban life, Economic Inquiry, 56, 114, 10.1111/ecin.12364 Gobster, 2020, Beyond proximity: Extending the “greening hypothesis” in the context of vacant lot stewardship, Landscape and Urban Planning, 197, 103773, 10.1016/j.landurbplan.2020.103773 Godron, 1981, Patches and structural components for a landscape ecology, BioScience, 31, 733, 10.2307/1308780 2013 Hastie, 2009, Multi-class AdaBoost, Statistics and Its Interface, 2, 349, 10.4310/SII.2009.v2.n3.a8 Herold, 2003, Spatial metrics and image texture for mapping urban land use, Photogrammetric Engineering & Remote Sensing, 69, 991, 10.14358/PERS.69.9.991 Hess, 2001, Measuring land use patterns for transportation research, Transportation Research Record, 1780, 17, 10.3141/1780-03 Hollander, 2019, Changing urban form in a shrinking city, Environment and Planning B: Urban Analytics and City Science, 46, 963 Hu, 2019, Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies, Land Use Policy, 82, 657, 10.1016/j.landusepol.2018.12.030 Hunter, 2007, Matplotlib: A 2D graphics environment, Computing in Science Engineering, 9, 90, 10.1109/MCSE.2007.55 Janssen, 2016, The challenges and limits of big data algorithms in technocratic governance, Government Information Quarterly, 33, 371, 10.1016/j.giq.2016.08.011 Jia, 2019, Landscape pattern indices for evaluating urban spatial morphology – A case study of Chinese cities, Ecological Indicators, 99, 27, 10.1016/j.ecolind.2018.12.007 Knaap, 2007, Measuring patterns of urban development: New intelligence for the war on Sprawl, Local Environment, 12, 239, 10.1080/13549830601183412 Lai, 2019, Topic modeling to discover the thematic structure and spatial-temporal patterns of building renovation and adaptive reuse in cities, Computers, Environment and Urban Systems, 78, 101383, 10.1016/j.compenvurbsys.2019.101383 Lemoine-Rodríguez, 2020, The global homogenization of urban form. An assessment of 194 cities across time, Landscape and Urban Planning, 204, 103949, 10.1016/j.landurbplan.2020.103949 Louppe, G. (2014). Understanding Random Forests: From Theory to Practice. ArXiv:1407.7502 [Stat]. http://arxiv.org/abs/1407.7502. Lowry, 2014, Comparing spatial metrics that quantify urban form, Computers, Environment and Urban Systems, 44, 59, 10.1016/j.compenvurbsys.2013.11.005 Mallach, A. (2012). Laying the groundwork for change: Demolition, urban strategy, and policy reform. Brookings Institution Report. September Washington DC Brookings Metropolitan Policy Program. https://www.brookings.edu/wp-content/uploads/2016/06/24-land-use-demolition-mallach.pdf. Mazumdar, 2018, The Built environment and social capital: A systematic review, Environment and Behavior, 50, 119, 10.1177/0013916516687343 McKinney, W. (2010). pandas: A Foundational Python Library for Data Analysis and Statistics. 9. Meerow, 2016, Defining urban resilience: A review, Landscape and Urban Planning, 147, 38, 10.1016/j.landurbplan.2015.11.011 Mora, 2018, Analysis of social networking service data for smart urban planning, Sustainability, 10, 4732, 10.3390/su10124732 Naik, 2017, Computer vision uncovers predictors of physical urban change, Proceedings of the National Academy of Sciences, 114, 7571, 10.1073/pnas.1619003114 Open Source Geospatial Foundation Project. (2020). QGIS Geographic Information System (Version 3.14) [Computer software]. Open Source Geospatial Foundation Project. https://qgis.org/en/site/. Ossola, 2019, Greening in style: Urban form, architecture and the structure of front and backyard vegetation, Landscape and Urban Planning, 185, 141, 10.1016/j.landurbplan.2019.02.014 Papadomanolaki, M., Verma, S., Vakalopoulou, M., Gupta, S., & Karantzalos, K. (2019). Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data. ArXiv:1910.07778 [Cs, Eess]. http://arxiv.org/abs/1910.07778. Paredes, 2017, The net benefit of demolishing dilapidated housing: The case of Detroit, Regional Science and Urban Economics, 66, 16, 10.1016/j.regsciurbeco.2017.05.009 Pedregosa, 2011, Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825 PostGIS. (2020). https://postgis.net. Python Software Foundation. (2020). Python Programming Language 3.6.10 Documentation. https://docs.python.org/3.6/. Reades, 2019, Understanding urban gentrification through machine learning, Urban Studies, 56, 922, 10.1177/0042098018789054 Reis, 2016, Spatial metrics to study urban patterns in growing and shrinking cities, Urban Geography, 37, 246, 10.1080/02723638.2015.1096118 Rodriguez-Galiano, 2012, An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93, 10.1016/j.isprsjprs.2011.11.002 Ronacher, A. (2014). Click Documentation (7.x). https://click.palletsprojects.com/en/7.x/. Sabouri, 2020, Exploring the relationship between ride-sourcing services and vehicle ownership, using both inferential and machine learning approaches, Landscape and Urban Planning, 198, 103797, 10.1016/j.landurbplan.2020.103797 Sapena, 2020, Analyzing Links between Spatio-Temporal Metrics of Built-Up Areas and Socio-Economic Indicators on a Semi-Global Scale, ISPRS International Journal of Geo-Information, 9, 436, 10.3390/ijgi9070436 Shafizadeh-Moghadam, 2019, Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches, Computers, Environment and Urban Systems, 76, 91, 10.1016/j.compenvurbsys.2019.04.005 Silverman, 2015, Municipal property acquisition patterns in a shrinking city: Evidence for the persistence of an urban growth paradigm in Buffalo, NY, Cogent Social Sciences, 1, 10.1080/23311886.2015.1012973 Song, 2016, Big data and data science: What should we teach?, Expert Systems, 33, 364, 10.1111/exsy.12130 Song, 2013, Comparing measures of urban land use mix, Computers, Environment and Urban Systems, 42, 1, 10.1016/j.compenvurbsys.2013.08.001 Soulé, 1986, What do genetics and ecology tell us about the design of nature reserves?, Biological Conservation, 35, 19, 10.1016/0006-3207(86)90025-X Steenberg, 2019, A spatio-temporal analysis of the relationship between housing renovation, socioeconomic status, and urban forest ecosystems, Environment and Planning B: Urban Analytics and City Science, 46, 1115 Stevenson, 2010, Using building permits to monitor disaster recovery: A spatio-temporal case study of coastal Mississippi following Hurricane Katrina, Cartography and Geographic Information Science, 37, 57, 10.1559/152304010790588052 Talen, 2018, The social context of U.S. built landscapes, Landscape and Urban Planning, 177, 266, 10.1016/j.landurbplan.2018.03.005 Taubenböck, 2019, Patterns of Eastern European urbanisation in the mirror of Western trends – Convergent, unique or hybrid?, Environment and Planning B: Urban Analytics and City Science, 46, 1206 The PostgreSQL Global Development Group. (2020). PostgreSQL: The world’s most advanced open source database. https://www.postgresql.org/. Thomas, J. V. (2010). Residential construction trends in America’s metropolitan regions. DIANE Publishing. http://books.google.com/books?hl=en&lr=&id=iCbbV0-JtGIC&oi=fnd&pg=PP1&dq=%225+-+Share+by+Unit%22+%22amount+of+permits+issued+by+central+cities+and+core+suburban+communities%22+%22In+fifteen+regions,+the+central+city+more+than+doubled+its+share+of%22+&ots=TMPmbr6Ht8&sig=TRNpgomIs5qDhNXgNYmlOwL_xtM. Tribby, 2017, Analyzing walking route choice through built environments using random forests and discrete choice techniques, Environment and Planning B: Urban Analytics and City Science, 44, 1145 Turner, M. G., & Gardner, R. H. (Eds.). (1991). Quantitative Methods in Landscape Ecology: The Analysis and Interpretation of Landscape Heterogeneity. Springer-Verlag. https://www.springer.com/gp/book/9780387942414. U.S. Census Bureau. (1994). Geographic Areas Reference Manual. U.S. Department of Commerce, Economics and Statistics Administration, Bureau of the Census. https://www2.census.gov/geo/pdfs/reference/GARM/GARMcont.pdf. van der Walt, 2011, The NumPy array: A structure for efficient numerical computation, Computing in Science Engineering, 13, 22, 10.1109/MCSE.2011.37 Waddell, P., & Besharati-Zadeh, A. (2020, November 26). A Comparison of Statistical and Machine Learning Algorithms for Predicting Rents in the San Francisco Bay Area. Transportation Research Board 98th Annual Meeting, Washington DC, United States. http://arxiv.org/abs/2011.14924. Walde, 2014, From land cover-graphs to urban structure types, International Journal of Geographical Information Science, 28, 584, 10.1080/13658816.2013.865189 Waskom, 2020, Seaborn (Version 0.11.0) [Computer software], Zenodo. Weber, 2006, Tearing the city down: Understanding demolition activity in gentrifying neighborhoods, Journal of Urban Affairs, 28, 19, 10.1111/j.0735-2166.2006.00257.x Yin, 2015, Housing abandonment and Demolition: Exploring the use of micro-level and multi-year models, ISPRS International Journal of Geo-Information, 4, 1184, 10.3390/ijgi4031184 Yoo, 2012, Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY, Landscape and Urban Planning, 107, 293, 10.1016/j.landurbplan.2012.06.009