طراحی شیء-گرا و تحت وب مبتنی بر علم شهروندی در جمع آوری اطلاعات مکانی بافت فرسوده شهری
محورهای موضوعی :
سیستم اطلاعات جغرافیایی
محمد حسن وحیدنیا
1
(استادیار گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، تهران، ایران. *(مسوول مکاتبات))
سید محمد ابراهیم موسوی
2
(کارشناس ارشد سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران، تهران، ایران.)
تاریخ دریافت : 1401/01/30
تاریخ پذیرش : 1401/03/18
تاریخ انتشار : 1401/03/01
کلید واژه:
اطلاعات جغرافیایی داوطلبانه (VGI),
سرویس های نقشه,
Web GIS,
مدل سازی,
بافت فرسوده,
چکیده مقاله :
زمینه و هدف: بسیاری از شهرهای کشور از جمله تهران با پدیده فرسایش محله های شهری مواجهند. این گونه بافت ها می تواند اثرات مخرب محیط زیستی، اقتصادی، و اجتماعی در پی داشته باشد. با توجه به وسعت زیاد بافت های فرسوده، جمع آوری و به هنگام سازی این میزان از داده توسط سازمان ها امری زمان بر و پرهزینه می باشد. بنابراین، هدف اصلی این پژوهش استفاده از ظرفیت مشارکت شهروندان یا علم شهروندی، به عنوان فرصتی مناسب برای گردآوری سریع و ارزان داده های مکانی بافت فرسوده می باشد.
روش بررسی: در سال های اخیر راهبرد GIS شهروند محور مطرح شده است و در بسیاری از کاربردها، داده های مکانی سازمانی جای خود را به اطلاعات جغرافیایی داوطلبانه (VGI) داده اند. در این پژوهش نیز یک طراحی شیء-گرا برای جمع آوری اطلاعات مکانی بافت فرسوده ارائه می شود. بر این اساس یک پیاده سازی نمونه تحت وب با تأکید بر مولفه های متن باز در دستور کار قرار می گیرد.
یافته ها: به کمک راهکار مطرح شده اولاً اطلاعات مکانی به صورت طبقه بندی شده و موضوعی قابل جمع آوری می باشند. 10 کلاس مختلف در رویکرد شیءگرا مد نظر قرار گرفت که از جمله می توان به مسیرهای نامطلوب، ساختمان های نا امن و قدیمی، و زمین های مخروبه و آلوده اشاره نمود. همچنین بر اساس چارچوب های متن باز از جمله جنگو (Django) و مولفه هایی چون GeoDjango، PostGIS و OpenLayers یک سامانه تحت وب کارآمد پیاده سازی شد.
بحث و نتیجه گیری: علوم شهروندی رویکردی نوین برای جمع آوری اطلاعات مکانی بافت فرسوده است. نتیجه اجرای روش به کار گرفته شده تهیه سریع و کم هزینه اطلاعات، و افزایش نقش نظارتی شهرداری ها به جای تولید داده را در پی دارد. تحلیل هزینه فایده نشان داد که در یک دوره یک ساله این رویکرد می تواند به طور تقریبی به کاهش 15 برابری هزینه های اخذ داده بیانجامد.
چکیده انگلیسی:
Background and Objective: Many cities in our country, including Tehran, are facing the phenomenon of obsolescence and inefficiency of urban neighborhoods. Such areas can have devastating environmental, economic, or social effects. In the current era, proper management of such urban spaces requires up-to-date and valid data. Due to the large size of outdated and inefficient neighborhoods, collecting and updating this amount of data by organizations is time-consuming and costly. Therefore, using the capacity of citizen participation is a good opportunity for city managers, which is the main purpose of this research.
Material and Methodology: Citizen-centered GIS strategy has been introduced in recent years, and in many applications, enterprise location data acquisition has been replaced by volunteered geographic information (VGI). This research presents an object-oriented design for collecting spatial information about urban deterioration. Accordingly, a web-based implementation based on open-source components is on the agenda.
Findings: With the help of the proposed solution, first, spatial information can be collected in a categorized and thematic manner. According to the definition of worn-out texture, ten different classes were considered in the object-oriented approach, which include undesirable paths, unsafe and old buildings, ruined and dirty lands, narrow access networks, and places for offenders and addicts. A web-based system was also implemented based on open-source frameworks such as Django and components such as GeoDjango, PostGIS, and OpenLayers.
Discussion and Conclusion: Citizen science is a new approach to collecting spatial information on worn-out tissue. The result of the implementation of the method used is the rapid and low-cost provider of information and enhances the role of experts in municipalities and urban design organizations, mainly professional supervisors instead of data providers. Cost-benefit analysis showed that in a one-year period, this approach could lead to an approximate savings of 15%.
منابع و مأخذ:
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Liu Y, Liu X, Gao S, Gong L, Kang C, Zhi Y, Chi G, Shi L. Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers. 2015 May 4;105(3):512-30.
Croitoru A, Crooks A, Radzikowski J, Stefanidis A. Geosocial gauge: a system prototype for knowledge discovery from social media. International Journal of Geographical Information Science. 2013 Dec 1;27(12):2483-508.
Jendryke M, Balz T, McClure SC, Liao M. Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai. Computers, Environment and Urban Systems. 2017 Mar 1; 62:99-112.
Sagl G, Delmelle E, Delmelle E. Mapping collective human activity in an urban environment based on mobile phone data. Cartography and Geographic Information Science. 2014 May 27;41(3):272-85.
Korson C. Political agency and citizen journalism: Twitter as a tool of evaluation. The Professional Geographer. 2015 Jul 3;67(3):364-73.
Janelle DG. Space-adjusting technologies and the social ecologies of place: review and research agenda. International Journal of Geographical Information Science. 2012 Dec 1;26(12):2239-51.
Polous K, Krisp JM, Meng L, Shrestha B, Xiao J. OpenEventMap: A volunteered location-based service. Cartographica: The International Journal for Geographic Information and Geovisualization. 2015 Dec;50(4):248-58.
Spyratos S, Stathakis D. Evaluating the services and facilities of European cities using crowdsourced place data. Environment and Planning B: Urban Analytics and City Science. 2018 Jul;45(4):733-50.
Yao Y, Liu X, Li X, Zhang J, Liang Z, Mai K, Zhang Y. Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. International Journal of Geographical Information Science. 2017 Jun 3;31(6):1220-44.
Foth M, Bajracharya B, Brown R, Hearn G. The Second Life of urban planning? Using NeoGeography tools for community engagement. Journal of location based services. 2009 Jun 1;3(2):97-117.
Gouveia C, Fonseca A. New approaches to environmental monitoring: the use of ICT to explore volunteered geographic information. GeoJournal. 2008 Aug;72(3):185-97.
Vahidnia MH, Hosseinali F, Shafiei M. Crowdsource mapping of target buildings in hazard: The utilization of smartphone technologies and geographic services. Applied Geomatics. 2020 Mar;12(1):3-14.
Hillen F, Höfle B. Geo-reCAPTCHA: Crowdsourcing large amounts of geographic information from earth observation data. International Journal of Applied Earth Observation and Geoinformation. 2015 Aug 1; 40:29-38.
Connors JP, Lei S, Kelly M. Citizen science in the age of neogeography: Utilizing volunteered geographic information for environmental monitoring. Annals of the Association of American Geographers. 2012 Nov 1;102(6):1267-89.
Brunsdon C, Comber L. Assessing the changing flowering date of the common lilac in North America: a random coefficient model approach. Geoinformatica. 2012 Oct;16(4):675-90.
Garcia‐Martí I, Zurita‐Milla R, Swart A, van den Wijngaard KC, van Vliet AJ, Bennema S, Harms M. Identifying environmental and human factors associated with tick bites using volunteered reports and frequent pattern mining. Transactions in GIS. 2017 Apr;21(2):277-99.
Hollenstein L, Purves R. Exploring place through user-generated content: Using Flickr tags to describe city cores. Journal of Spatial Information Science. 2010 Dec 31(1):21-48.
Goodchild MF. Citizens as sensors: the world of volunteered geography. GeoJournal. 2007 Aug;69(4):211-21.
Rahmatizadeh S, Rajabifard A, Kalantari M. A conceptual framework for utilising VGI in land administration. Land Use Policy. 2016 Nov 1; 56:81-9.
Senaratne H, Mobasheri A, Ali AL, Capineri C, Haklay M. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science. 2017 Jan 2;31(1):139-67.
Lodigiani C, Melchiori M. A pagerank-based reputation model for VGI data. Procedia Computer Science. 2016 Jan 1; 98:566-71.
Aissi S, Sboui T. Towards Evaluating Geospatial Metadata Quality in the Context of VGI. Procedia Computer Science. 2017 Jan 1; 109:686-91.
Naghavi M, Alesheikh AA, Hakimpour F, Vahidnia MH, Vafaeinejad A. VGI-based spatial data infrastructure for land administration. Land Use Policy. 2022 Mar 1; 114:105969.
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Hashemzadeh, F., Ebizadeh, S., SafarAlizadeh, E. Identification and Prioritization of the Most Important Factors to Advance Urban Management Plans in the Area of Old Texture (Case Study: Maku city). Research and Urban Planning, 2020; 11(40): 137-154.
Taghavi, T., Ebrahimi jamnani, L., Bozorgmehr, K., Haghzad, A. Analysis of the effect of healthy city variables on improvement and renovation of worn tissue (Case study; dilapidated neighborhoods of Ghaemshahr). Geographical Journal of Tourism Space, 2020; 9(36): 131-153.
Jang SY, Choe Y, Kim SA. Place engine: a dynamic model of integrated human-oriented GIS and urban media. Procedia-Social and Behavioral Sciences. 2015 Feb 12; 174:3314-3321.
Fontes D, Fonte C, Cardoso A. A web GIS-based platform to assist authorities in emergency response using VGI and sensor data. In2017 4th Experiment@ International Conference (exp. at'17) 2017 Jun 6 (pp. 127-128). IEEE.
Vahidnia MH, Vahidi H. Open Community-Based Crowdsourcing Geoportal for Earth Observation Products: A Model Design and Prototype Implementation. ISPRS International Journal of Geo-Information. 2021 Jan;10(1):24.
Liu Y, Liu X, Gao S, Gong L, Kang C, Zhi Y, Chi G, Shi L. Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers. 2015 May 4;105(3):512-30.
Croitoru A, Crooks A, Radzikowski J, Stefanidis A. Geosocial gauge: a system prototype for knowledge discovery from social media. International Journal of Geographical Information Science. 2013 Dec 1;27(12):2483-508.
Jendryke M, Balz T, McClure SC, Liao M. Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai. Computers, Environment and Urban Systems. 2017 Mar 1; 62:99-112.
Sagl G, Delmelle E, Delmelle E. Mapping collective human activity in an urban environment based on mobile phone data. Cartography and Geographic Information Science. 2014 May 27;41(3):272-85.
Korson C. Political agency and citizen journalism: Twitter as a tool of evaluation. The Professional Geographer. 2015 Jul 3;67(3):364-73.
Janelle DG. Space-adjusting technologies and the social ecologies of place: review and research agenda. International Journal of Geographical Information Science. 2012 Dec 1;26(12):2239-51.
Polous K, Krisp JM, Meng L, Shrestha B, Xiao J. OpenEventMap: A volunteered location-based service. Cartographica: The International Journal for Geographic Information and Geovisualization. 2015 Dec;50(4):248-58.
Spyratos S, Stathakis D. Evaluating the services and facilities of European cities using crowdsourced place data. Environment and Planning B: Urban Analytics and City Science. 2018 Jul;45(4):733-50.
Yao Y, Liu X, Li X, Zhang J, Liang Z, Mai K, Zhang Y. Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. International Journal of Geographical Information Science. 2017 Jun 3;31(6):1220-44.
Foth M, Bajracharya B, Brown R, Hearn G. The Second Life of urban planning? Using NeoGeography tools for community engagement. Journal of location based services. 2009 Jun 1;3(2):97-117.
Gouveia C, Fonseca A. New approaches to environmental monitoring: the use of ICT to explore volunteered geographic information. GeoJournal. 2008 Aug;72(3):185-97.
Vahidnia MH, Hosseinali F, Shafiei M. Crowdsource mapping of target buildings in hazard: The utilization of smartphone technologies and geographic services. Applied Geomatics. 2020 Mar;12(1):3-14.
Hillen F, Höfle B. Geo-reCAPTCHA: Crowdsourcing large amounts of geographic information from earth observation data. International Journal of Applied Earth Observation and Geoinformation. 2015 Aug 1; 40:29-38.
Connors JP, Lei S, Kelly M. Citizen science in the age of neogeography: Utilizing volunteered geographic information for environmental monitoring. Annals of the Association of American Geographers. 2012 Nov 1;102(6):1267-89.
Brunsdon C, Comber L. Assessing the changing flowering date of the common lilac in North America: a random coefficient model approach. Geoinformatica. 2012 Oct;16(4):675-90.
Garcia‐Martí I, Zurita‐Milla R, Swart A, van den Wijngaard KC, van Vliet AJ, Bennema S, Harms M. Identifying environmental and human factors associated with tick bites using volunteered reports and frequent pattern mining. Transactions in GIS. 2017 Apr;21(2):277-99.
Hollenstein L, Purves R. Exploring place through user-generated content: Using Flickr tags to describe city cores. Journal of Spatial Information Science. 2010 Dec 31(1):21-48.
Goodchild MF. Citizens as sensors: the world of volunteered geography. GeoJournal. 2007 Aug;69(4):211-21.
Rahmatizadeh S, Rajabifard A, Kalantari M. A conceptual framework for utilising VGI in land administration. Land Use Policy. 2016 Nov 1; 56:81-9.
Senaratne H, Mobasheri A, Ali AL, Capineri C, Haklay M. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science. 2017 Jan 2;31(1):139-67.
Lodigiani C, Melchiori M. A pagerank-based reputation model for VGI data. Procedia Computer Science. 2016 Jan 1; 98:566-71.
Aissi S, Sboui T. Towards Evaluating Geospatial Metadata Quality in the Context of VGI. Procedia Computer Science. 2017 Jan 1; 109:686-91.
Naghavi M, Alesheikh AA, Hakimpour F, Vahidnia MH, Vafaeinejad A. VGI-based spatial data infrastructure for land administration. Land Use Policy. 2022 Mar 1; 114:105969.
