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Kentsel Aktivitenin Ölçülmesi

Yıl 2021, Cilt: 2 Sayı: 1, 355 - 376, 31.03.2021

Öz

Kentsel planlama ve tasarımların başarılı olmaları için planlamacıların kamusal alanları kullananlar ve kullanım durumları hakkında varsayımlarda bulunmasını gerektirir. Bu nedenle, şehir planlamacılarının kentsel alanlarda meydana gelen etkinlikleri kaydetmeleri ve ölçmeleri gerekir. Geleneksel olarak, planlamacılar kentsel etkinlikleri yakalamak için anketler ve gözlemler kullanıyorlardı. Bununla beraber, teknolojik gelişmelerle birlikte, şehir planlamacıları daha uzun zaman ve daha geniş mekânları kapsayan mekânsal-zamansal verilere erişebilmeye başladılar. Bu incelemede kentsel aktivitenin kaydedilebilmesi için kullanılabilecek yöntemler beş başlıkta toplanmıştır: geleneksel yöntemler, araştırmacılar tarafından yerleştirilen sensörler tarafından kaydedilen yöntemler, kullanıcılar tarafından sensörlerin taşınmasıyla kaydedilen yöntemler, akıllı telefonlarla kaydedilen yöntemler ve büyük veri yöntemleri. Tartışılan yöntemler, kentsel aktivitenin kaydedilmesi için büyük potansiyel taşımasına rağmen gizlilik sorunları, örneklem kısıtlaması, bağlamın bilinmemesi ve teknik altyapı ihtiyacı gibi zorlukları barındırmaktadır. Bu yöntemlerden başarılı bir şekilde yararlanabilmek için verinin doğruluğunu iyileştirilmesi, bağlamı çıkarımsamak için değişik yöntemleri birleştirilmesi, teknik altyapı oluşturabilmek için değişik işbirlikleri yapılması ya da verinin hazır olarak satın alınması gibi daha fazla çabaya ihtiyaç vardır.

Kaynakça

  • Ahas, R., Aasa, A., Mark, Ü., Pae, T., & Kull, A. (2007). Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Management 28(3), 898 –910.
  • Balaban, Ö. (2019). Understanding urban leisure walking behaviour: correlations between neighbourhood features and fitness tracking application data, [Doctoral disertation, Singapore University of Technology and Design]
  • Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment 2(3), 199 –219. ISSN: 1361-9209.
  • Chen, C., Ma, J., Susilo, Y., Liu, Y., & Wang, M. (2016). The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C: Emerging Technologies, 68, 285–299.
  • Danalet, A., Tinguely, L., de Lapparent, M., & Bierlaire, M. (2016). Location choice with longitudinal WiFi data. Journal of Choice Modelling, 18, 1– 17.
  • De Montigny, L., Ling, R., & Zacharias, J. (2012). The effects of weather on walking rates in nine cities. Environment and Behavior 44(6), 821–840.
  • Evenson, K. R., Herring, A. H., & Huston, S. L. (2005). Evaluating change in physical activity with the building of a multi-use trail. American Journal of Preventive Medicine, 28(2), Supplement 2. Active Living Research, 177 –185.
  • Fitbit (2018). SmartTrack. https://www.fitbit.com/smarttrack
  • Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature 453(7196), 779-782.
  • Hampton, K. N., Livio, O., & Sessions Goulet, L. (2010). The social life of wireless urban spaces: Internet use, social networks, and the public realm. Journal of Communication 60(4), 701–722.
  • Handy, S. L. (1996). Urban form and pedestrian choices: study of Austin neighborhoods. Transportation Research Record: Journal of the Transportation Research Board, 1552(1), 135–144.
  • Huntsinger, L. F., & Donnelly, R. (2014). Reconciliation of regional travel model and passive device tracking data. Tech. rep.
  • Jacobs, J. (1961). The death and life of great american cities. Random House.
  • Kang, C., Sobolevsky, S., Liu, Y., & Ratti, C. (2013, August). Exploring human movements in Singapore: a comparative analysis based on mobile phone and taxicab usages. Proceedings of the 2nd ACMSIGKDD International Workshop on Urban Computing. ACM.
  • Krizek, K., Forysth, A., & Slotterback, C. S. (2009). Is there a role for evidencebased practice in urban planning and policy? Planning Theory & Practice (10)4, 459–478.
  • Lane, N. D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., ... & Campbell, A. (2011, May). Bewell: A smartphone application to monitor, mode land promote wellbeing. 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, 23–26.
  • Lee, K., & Sener, I. N. (2017). emerging data mining for pedestrian and bicyclist monitoring: A literature review report. Safety through Disruption (Safe-D) National University Transportation Center (UTC) Program.
  • Marshall, S. (2012). Science, pseudo-science and urban design. Urban Design International (17)4, 257–271.
  • Monnot, B., Wilhelm, E., Piliouras, G., Zhou, Y., Dahlmeier, D., Lu, H. Y., & Jin, W. (2016). Inferring activities and optimal trips: Lessons from Singapore’s National Science Experiment. Complex Systems Design & Management Asia. Cham: Springer, 247–264.
  • Nelson, T., & Winters, M. (2021). Using strava data for active transportation planning. https://medium.com/strava-metro/using-strava-data-foractive-transportation-planning-1d6bc63e0e77 (01.03.2021).
  • Noulas, A., Scellato, S., Lathia, N., & Mascolo, C. (2012, December). Mining user mobility features for next place prediction in location-based services. 2012 IEEE 12th international conference on data mining, 1038-1043. IEEE.
  • Ohlms, P. B., Dougald, L. E., & MacKnight, H. E. (2018). Assessing the feasibility of a Pedestrian and Bicycle Count Program in Virginia (VTRC 19-R4). Virginia Transportation Research Council.
  • Pesce, M., & Tonkin, J. (2006). BlueStates, Interactive City Artworks. http://2006.01sj.org/content/view/377/49/ (10.12.2018)
  • Richardson, A. J., Ampt, E. S., & Meyburg, A. H. (1995). Survey methods for transport planning, 75-145. Melbourne: Eucalyptus Press.
  • Sands, M. (2015). ’Eyes’ on the street: What public camera feed data can teachus about civic and political behavior.
  • Schneider, R. J., Arnold, L. S., & Ragland, D. R. (2009). Methodology for counting pedestrians at intersections: Use of automated counters to extrapolate weekly volumes from short manual counts. Transportation
  • Research Record: Journal of the Transportation Research Board, 2140, 1– 12.
  • Seer, S., Brändle, N., & Ratti, C. (2014). Kinects and human kinetics: Anew approach for studying pedestrian behavior. Transportation Research Part C: Emerging Technologies, 48, 212–228.
  • Shlayan, N., Kurkcu, A., & Ozbay, K. (2016, November). Exploring pedestrian Bluetooth and WiFi detection at public transportation terminals. 2016 IEEE 19th InternationalConference on Intelligent Transportation Systems (ITSC), 229–234.
  • Šileryte, R. (2015). Analysis of urban space networks for recreational purposes based on mobile sports tracking application data.
  • Stopher, P., FitzGerald, C., & Xu, M. (2007). Assessing the accuracy of the Sydney Household Travel Survey with GPS. Transportation (34)6, 723– 741.
  • Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going?. Transportation Research Part A: Policy and Practice, 41(5), 367- 381.
  • Tomarchio, L., Tuncer, B., You, L., & Klein, B. (2016). Mapping planned and emerging art places in singapore through social media feeds. Proceedings of eCAADe 2016, 34th Annual Conference on Education and Research in Computer Aided Architectural Design in Europe, 437–446.
  • Transportation Research, engineering national academies of sciences, and medicine board (2014). Guidebook on Pedestrian and Bicycle Volume Data Collection. Ed. by Paul Ryus etal. Washington, DC: The National Academies Press.
  • Tudor-Locke, C., Ainsworth, B. E., Thompson, R. W., & Matthews, C. E. (2002). Comparison of pedometer and accelerometer measures of free-living physical activity. Medicine and Science in Sports and Exercise (34)12, 2045–2051.
  • Tunçer, B., & You, L. (2017). Informed design platform multi-modal data to support urban design decision making. International Conference on Education and Research in Computer Aided Architectural Design in Europe (1)35, 545–552.
  • Turner, S., Sener, I. N., Martin, M. E., Das, S., Hampshire, R. C., Fitzpatrick, K., ... & Wijesundera, R. K. (2017). Synthesis of methods for estimating pedestrian and bicyclist exposure to risk at areawide levels and on specific transportation facilities (No. FHWA-SA-17-041). United States. Federal Highway Administration. Office of Safety.
  • van der Spek, S. (2008). Spatial Metro-Tracking pedestrians in historic city centres. Research in Urbanism Series, 1, 77–97. ISSN: 1879-8217.
  • Vanky, A. P., Verma, S. K., Courtney, T. K., Santi, P., & Ratti, C. (2017). Effect of weather on pedestrian trip count and duration: City-scale evaluations using mobile phone application data. Preventive Medicine Reports, 8, 30– 37.
  • Whyte, W. H. (1980). The social life of small urban spaces. Conservation Foundation.
  • Wolf, J., Oliveira, M., & Thompson, M. (2003). Impact of underreporting on mileage and travel time estimates: Results from global positioning systemenhanced household travel survey. Transportation Research Record: Journal of the Transportation Research Board, 1854(1), 189–198.
  • Yang, J. (2009, October). Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones. Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics. IMCE ’09. Beijing, China: ACM, 1–10.
  • Yoshimura, Y., Girardin, F., Carrascal, J. P., Ratti, C., & Blat, J. (2012). New tools for studying visitor behaviours in museums: A case study at the Louvre. Springer-Verlag.
  • Yoshimura, Y., Amini, A., Sobolevsky, S., Blat, J., & Ratti, C . (2017). Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring. Applied Geography, 81, 43–51.

Measuring Urban Activities

Yıl 2021, Cilt: 2 Sayı: 1, 355 - 376, 31.03.2021

Öz

Successful urban planning and design projects require planners to make assumptions about users and use cases for urban spaces. Therefore, urban planners need to capture activities that happened in the urban spaces. Traditionally, planners relied on surveys and observation to capture urban activities. However, with technological advances, urban planners can access spatiotemporal data covering longer periods of time and space. In this paper, we reviewed the methods that can be used to measure urban activities under five sections: traditional methods, measuring with the sensors installed by surveyors, measuring with the sensors carried by participants, smartphone as sensors and big data. Although the methods discussed have great potential for recording urban activity, they have difficulties such as privacy issues, sampling limitations, lack of knowledge of the context and the need for technical infrastructure. In order to benefit from these methods successfully, more efforts are needed such as improving the accuracy of the data, combining different methods to infer the context, making different collaborations to create technical infrastructure or purchasing the data readily.

Kaynakça

  • Ahas, R., Aasa, A., Mark, Ü., Pae, T., & Kull, A. (2007). Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Management 28(3), 898 –910.
  • Balaban, Ö. (2019). Understanding urban leisure walking behaviour: correlations between neighbourhood features and fitness tracking application data, [Doctoral disertation, Singapore University of Technology and Design]
  • Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment 2(3), 199 –219. ISSN: 1361-9209.
  • Chen, C., Ma, J., Susilo, Y., Liu, Y., & Wang, M. (2016). The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C: Emerging Technologies, 68, 285–299.
  • Danalet, A., Tinguely, L., de Lapparent, M., & Bierlaire, M. (2016). Location choice with longitudinal WiFi data. Journal of Choice Modelling, 18, 1– 17.
  • De Montigny, L., Ling, R., & Zacharias, J. (2012). The effects of weather on walking rates in nine cities. Environment and Behavior 44(6), 821–840.
  • Evenson, K. R., Herring, A. H., & Huston, S. L. (2005). Evaluating change in physical activity with the building of a multi-use trail. American Journal of Preventive Medicine, 28(2), Supplement 2. Active Living Research, 177 –185.
  • Fitbit (2018). SmartTrack. https://www.fitbit.com/smarttrack
  • Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature 453(7196), 779-782.
  • Hampton, K. N., Livio, O., & Sessions Goulet, L. (2010). The social life of wireless urban spaces: Internet use, social networks, and the public realm. Journal of Communication 60(4), 701–722.
  • Handy, S. L. (1996). Urban form and pedestrian choices: study of Austin neighborhoods. Transportation Research Record: Journal of the Transportation Research Board, 1552(1), 135–144.
  • Huntsinger, L. F., & Donnelly, R. (2014). Reconciliation of regional travel model and passive device tracking data. Tech. rep.
  • Jacobs, J. (1961). The death and life of great american cities. Random House.
  • Kang, C., Sobolevsky, S., Liu, Y., & Ratti, C. (2013, August). Exploring human movements in Singapore: a comparative analysis based on mobile phone and taxicab usages. Proceedings of the 2nd ACMSIGKDD International Workshop on Urban Computing. ACM.
  • Krizek, K., Forysth, A., & Slotterback, C. S. (2009). Is there a role for evidencebased practice in urban planning and policy? Planning Theory & Practice (10)4, 459–478.
  • Lane, N. D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., ... & Campbell, A. (2011, May). Bewell: A smartphone application to monitor, mode land promote wellbeing. 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, 23–26.
  • Lee, K., & Sener, I. N. (2017). emerging data mining for pedestrian and bicyclist monitoring: A literature review report. Safety through Disruption (Safe-D) National University Transportation Center (UTC) Program.
  • Marshall, S. (2012). Science, pseudo-science and urban design. Urban Design International (17)4, 257–271.
  • Monnot, B., Wilhelm, E., Piliouras, G., Zhou, Y., Dahlmeier, D., Lu, H. Y., & Jin, W. (2016). Inferring activities and optimal trips: Lessons from Singapore’s National Science Experiment. Complex Systems Design & Management Asia. Cham: Springer, 247–264.
  • Nelson, T., & Winters, M. (2021). Using strava data for active transportation planning. https://medium.com/strava-metro/using-strava-data-foractive-transportation-planning-1d6bc63e0e77 (01.03.2021).
  • Noulas, A., Scellato, S., Lathia, N., & Mascolo, C. (2012, December). Mining user mobility features for next place prediction in location-based services. 2012 IEEE 12th international conference on data mining, 1038-1043. IEEE.
  • Ohlms, P. B., Dougald, L. E., & MacKnight, H. E. (2018). Assessing the feasibility of a Pedestrian and Bicycle Count Program in Virginia (VTRC 19-R4). Virginia Transportation Research Council.
  • Pesce, M., & Tonkin, J. (2006). BlueStates, Interactive City Artworks. http://2006.01sj.org/content/view/377/49/ (10.12.2018)
  • Richardson, A. J., Ampt, E. S., & Meyburg, A. H. (1995). Survey methods for transport planning, 75-145. Melbourne: Eucalyptus Press.
  • Sands, M. (2015). ’Eyes’ on the street: What public camera feed data can teachus about civic and political behavior.
  • Schneider, R. J., Arnold, L. S., & Ragland, D. R. (2009). Methodology for counting pedestrians at intersections: Use of automated counters to extrapolate weekly volumes from short manual counts. Transportation
  • Research Record: Journal of the Transportation Research Board, 2140, 1– 12.
  • Seer, S., Brändle, N., & Ratti, C. (2014). Kinects and human kinetics: Anew approach for studying pedestrian behavior. Transportation Research Part C: Emerging Technologies, 48, 212–228.
  • Shlayan, N., Kurkcu, A., & Ozbay, K. (2016, November). Exploring pedestrian Bluetooth and WiFi detection at public transportation terminals. 2016 IEEE 19th InternationalConference on Intelligent Transportation Systems (ITSC), 229–234.
  • Šileryte, R. (2015). Analysis of urban space networks for recreational purposes based on mobile sports tracking application data.
  • Stopher, P., FitzGerald, C., & Xu, M. (2007). Assessing the accuracy of the Sydney Household Travel Survey with GPS. Transportation (34)6, 723– 741.
  • Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going?. Transportation Research Part A: Policy and Practice, 41(5), 367- 381.
  • Tomarchio, L., Tuncer, B., You, L., & Klein, B. (2016). Mapping planned and emerging art places in singapore through social media feeds. Proceedings of eCAADe 2016, 34th Annual Conference on Education and Research in Computer Aided Architectural Design in Europe, 437–446.
  • Transportation Research, engineering national academies of sciences, and medicine board (2014). Guidebook on Pedestrian and Bicycle Volume Data Collection. Ed. by Paul Ryus etal. Washington, DC: The National Academies Press.
  • Tudor-Locke, C., Ainsworth, B. E., Thompson, R. W., & Matthews, C. E. (2002). Comparison of pedometer and accelerometer measures of free-living physical activity. Medicine and Science in Sports and Exercise (34)12, 2045–2051.
  • Tunçer, B., & You, L. (2017). Informed design platform multi-modal data to support urban design decision making. International Conference on Education and Research in Computer Aided Architectural Design in Europe (1)35, 545–552.
  • Turner, S., Sener, I. N., Martin, M. E., Das, S., Hampshire, R. C., Fitzpatrick, K., ... & Wijesundera, R. K. (2017). Synthesis of methods for estimating pedestrian and bicyclist exposure to risk at areawide levels and on specific transportation facilities (No. FHWA-SA-17-041). United States. Federal Highway Administration. Office of Safety.
  • van der Spek, S. (2008). Spatial Metro-Tracking pedestrians in historic city centres. Research in Urbanism Series, 1, 77–97. ISSN: 1879-8217.
  • Vanky, A. P., Verma, S. K., Courtney, T. K., Santi, P., & Ratti, C. (2017). Effect of weather on pedestrian trip count and duration: City-scale evaluations using mobile phone application data. Preventive Medicine Reports, 8, 30– 37.
  • Whyte, W. H. (1980). The social life of small urban spaces. Conservation Foundation.
  • Wolf, J., Oliveira, M., & Thompson, M. (2003). Impact of underreporting on mileage and travel time estimates: Results from global positioning systemenhanced household travel survey. Transportation Research Record: Journal of the Transportation Research Board, 1854(1), 189–198.
  • Yang, J. (2009, October). Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones. Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics. IMCE ’09. Beijing, China: ACM, 1–10.
  • Yoshimura, Y., Girardin, F., Carrascal, J. P., Ratti, C., & Blat, J. (2012). New tools for studying visitor behaviours in museums: A case study at the Louvre. Springer-Verlag.
  • Yoshimura, Y., Amini, A., Sobolevsky, S., Blat, J., & Ratti, C . (2017). Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring. Applied Geography, 81, 43–51.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mimarlık
Bölüm Araştırma Makaleleri
Yazarlar

Özgün Balaban

Yayımlanma Tarihi 31 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 2 Sayı: 1

Kaynak Göster

APA Balaban, Ö. (2021). Measuring Urban Activities. Journal of Computational Design, 2(1), 355-376.

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JCoDe makaleleri "Creative Commons Attribution-NonCommercial 4.0 International License" altında yayınlanmaktadır.