Difference between revisions of "Designing Low-Carbon Urban Form"

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Latest revision as of 07:46, 9 September 2021

Urban form, the physical form of cities, has important implications of energy use and GHG emissions, for example sprawled cities can induce mobility demand. ML can help analyze energy use implications of different urban forms, simulate urban development pathways and (re-)designing neighborhoods by finding patterns in urban form data.

Background Readings[edit | edit source]

Analyzing energy use implications of different urban forms[edit | edit source]

  • "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand" (2017)[1]: This study using neural networks finds that for the city of Porto, urban form explains about 78% of the variation of energy use.
  • "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo" (2018)[2]: This study investigate how the distance to the central areas in the city of Oslo is related to the distance traveled, and makes urban form proposal for driving reduction.
  • "Examining threshold effects of built environment elements on travel-related carbon-dioxide emissions" (2019)[3]:
  • "Non-linear relationships between built environment characteristics and electric-bike ownership in Zhongshan, China" (2019)[4]:
  • "Exploring the Nonlinear Relationship between the Built Environment and Active Travel in the Twin Cities" (2020)[5]:

Simulating urban development pathways[edit | edit source]

  • "A scenario-based approach for assessing the energy performance of urban development pathways" (2018)[6]:
  • "Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks" (2019)[7]:
  • "Modeling Urbanization Patterns with Generative Adversarial Networks" (2018)[8]:

(Re-)designing neighborhoods[edit | edit source]

  • "Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian-scale neighborhood planning" (2020)[9]:
  • "The evaluation of the spatial integration of station areas via the node place model; an application to subway station areas in Tehran"[10] (2015):


Community[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

Future Directions[edit | edit source]

References[edit | edit source]

  1. Silva, Mafalda C.; Horta, Isabel M.; Leal, Vítor; Oliveira, Vítor (2017-09). "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand". Applied Energy. 202: 386–398. doi:10.1016/j.apenergy.2017.05.113. ISSN 0306-2619. Check date values in: |date= (help)
  2. Ding, Chuan; Cao, Xinyu (Jason); Næss, Petter (2018-04-01). "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo". Transportation Research Part A: Policy and Practice. 110: 107–117. doi:10.1016/j.tra.2018.02.009. ISSN 0965-8564.
  3. Wu, Xinyi; Tao, Tao; Cao, Jason; Fan, Yingling; Ramaswami, Anu (2019-10-01). "Examining threshold effects of built environment elements on travel-related carbon-dioxide emissions". Transportation Research Part D: Transport and Environment. 75: 1–12. doi:10.1016/j.trd.2019.08.018. ISSN 1361-9209.
  4. Ding, Chuan; Cao, Xinyu; Dong, Meixuan; Zhang, Yi; Yang, Jiawen (2019-10-01). "Non-linear relationships between built environment characteristics and electric-bike ownership in Zhongshan, China". Transportation Research Part D: Transport and Environment. 75: 286–296. doi:10.1016/j.trd.2019.09.005. ISSN 1361-9209.
  5. Tao, Tao; Wu, Xinyi; Cao, Jason; Fan, Yingling; Das, Kirti; Ramaswami, Anu (2020-05-26). "Exploring the Nonlinear Relationship between the Built Environment and Active Travel in the Twin Cities". Journal of Planning Education and Research: 0739456X20915765. doi:10.1177/0739456X20915765. ISSN 0739-456X.
  6. Silva, Mafalda; Leal, Vítor; Oliveira, Vítor; Horta, Isabel M. (2018-07-01). "A scenario-based approach for assessing the energy performance of urban development pathways". Sustainable Cities and Society. 40: 372–382. doi:10.1016/j.scs.2018.01.028. ISSN 2210-6707.
  7. Albert, Adrian; Kaur, Jasleen; Strano, Emanuele; Gonzalez, Marta (2019-07-22). "Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks". arXiv:1907.09543 [cs, stat].
  8. Albert, A.; Strano, E.; Kaur, J.; González, M. (2018-07). "Modeling Urbanization Patterns with Generative Adversarial Networks". IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium: 2095–2098. doi:10.1109/IGARSS.2018.8518032. Check date values in: |date= (help)
  9. Zhang, Wenjia; Zhao, Yajing; (Jason) Cao, Xinyu; Lu, Daming; Chai, Yanwei (2020-09-01). "Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian-scale neighborhood planning". Transportation Research Part D: Transport and Environment. 86: 102445. doi:10.1016/j.trd.2020.102445. ISSN 1361-9209.
  10. Monajem, Saeed; Ekram Nosratian, Farzan (2015-10-01). "The evaluation of the spatial integration of station areas via the node place model; an application to subway station areas in Tehran". Transportation Research Part D: Transport and Environment. 40: 14–27. doi:10.1016/j.trd.2015.07.009. ISSN 1361-9209.