تعيين كاربري اجتماعي در محيط‌هاي شهري بر مبناي بعد معنايي داده‌هاي شبكه‌هاي اجتماعي مكان‌مبنا

Characterization of Social Land use in Urban Environments Based on the Semantic Dimension of Location Based Social Networks’ Data

شناخت محيط‌هاي شهري و درك رفتار حركتي شهروندان يك زمينه تحقيقاتي مهم در حوزه تحليل داده‌هاي مكاني است. شبكه‌هاي اجتماعي مكان‌مبنا داده‌هاي به‌روز، غني و عظيمي را كه كاربران به‌طور صادقانه‌اي از رفتار مكاني، زماني و معنايي خود به اشتراك مي‌گذارند، ثبت و جمع‌آوري مي‌كنند. در اين تحقيق تلاش شده است بر مبناي داده‌هاي مكاني و معنايي شبكه‌هاي اجتماعي مكان‌مبنا، محيط‌ هاي شهري بر مبناي كاربري‌هاي اجتماعي خوشه‌بندي شوند. بدون ترديد ساختار فيزيكي و همچنين كاربري يك منطقه شهري بر روي رفتار مكاني شهروندان تأثير مي‌گذارد و اين تأثير به داده‌هاي شبكه‌هاي اجتماعي مكان‌مبنا نيز سرايت مي‌كند. در اين راستا در اولين گام با به‌كارگيري يك الگوريتم خوشه‌بندي مبتني بر شبكه عصبي رقابتي (SOM) داده‌هاي مكاني كاربران خوشه‌بندي مي‌گردد. سپس با رسم دياگرام ورونوي بر روي مراكز خوشه‌ها، محيط شهري به چند منطقه افراز شده و داده‌هايي كه كاربران در هر منطقه ثبت كرده‌اند مشخص مي‌شود. پس از استخراج درصد هر گروه از داده‌هاي معنايي و با در نظر گرفتن وزن هر يك از اين گروه‌ها، با بهره‌گيري از الگوريتم فرا ابتكاري ژنتيك شاخص بهينه‌اي براي تعيين كاربري اجتماعي محاسبه مي‌گردد. سپس با استفاده از يك الگوريتم خوشه‌بندي بر مبناي شاخص تعيين شده، به هر منطقه يك بعد معنايي كه نماينده كاربري اجتماعي آن منطقه است، نسبت داده شد. به‌منظور ارزيابي روش پيشنهادي، نمودار تغييرات زماني تعداد داده‌ها در طول شبانه‌روز براي پنج روز كاري هفته و دو روز پايان هفته براي هر گروه از داده‌هاي معنايي رسم شده و براي خوشه‌هاي شناسايي‌شده از روش پيشنهادي نيز استخراج گرديد. سپس از همبستگي بين اين نمودارها به‌عنوان شاخص ارزيابي روش پيشنهادي استفاده شد. نتايج حاصل از اين تحقيق، بيانگر پتانسيل بالاي شبكه‌هاي اجتماعي مكان‌مبنا براي شناخت محيط‌هاي شهري مي‌باشد.

Recognizing the urban environments and understanding the citizens’ motion behavior is an important research field in the area of spatial data analysis. The location-based social networks record and gather update, rich, and enormous data that users share them honestly through their spatial, temporal and semantic behavior. Undoubtedly the physical structure of an urban area as well as its land use impress the spatial behavior of its citizens and this impression propagates to the data of location-based social networks. Because of that, nowadays, researchers use the users’ data in location-based social networks in order to recognize urban environments. In this research, we attempted to cluster urban environments based on social land uses by using the location-based social networks’ spatial and semantic data. In this regard, in the first step, the spatial data of users are clustered by employing a clustering algorithm that is based on a competitive neural network (SOM). To cluster the spatial data of users, we first should calculate the optimal number of clusters. In this regard, Elbow chart was used as DB index. Then, the urban environment is partitioned into several regions by drawing the Voronoi diagram on the cluster centers and the data which users have been recorded in each region are identified. The number of data available in each region was computed for semantic categories separately, then the vector of each region was normalized. Similarly, these operations were repeated for all data in whole urban environment and the. The initial idea is usage of the abundance of each category of semantic data; however, this criterion cannot determine the land use of a region properly; because it is possible that users share more information about, for example, creation places than residential ones. Finally after extracting the percentage of the different groups of semantic data and by considering the weight of each group, a semantic dimension that is the representative of the region’s social land use was assigned to each region by taking advantage of a clustering algorithm based on the semantic dimension of users’ data. To evaluate the proposed method, the number of data in each category was calculated for every 15 minutes of a day to verify the validity of data that users share about their activities in the foursquare social network. To more accurate study, the working days and weekend days were studied separately; i.e. for each category, we formed a vector with 192 members. The chart of temporal variations of data numbers during a day (24 hours) was plotted for clusters identified from proposed method too. Then, the correlation among these charts was used as the evaluation index of the proposed method. This research and the performed evaluation show that the big data of social networks are not only low cost and updated but also shared by citizens honestly and have suitable validity. Also, the urban regions with common or similar social land uses have spatial continuity. The results of the research show the high potential of the location-based social networks to recognize urban environments.