詳細な滞留者属性情報の組み込みによる時空間人口統計データの高度化

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書誌事項

タイトル別名
  • ADVANCED SPATIOTEMPORAL DEMOGRAPHIC DATA INTEGRATED WITH DETAILED ATTRIBUTE INFORMATION OF PEOPLE
  • 詳細な滞留者属性情報の組み込みによる時空間人口統計データの高度化 : パーソントリップ調査データとモバイル空間統計の統合方法について
  • ショウサイ ナ タイリュウシャ ゾクセイ ジョウホウ ノ クミコミ ニ ヨル ジクウカン ジンコウ トウケイ データ ノ コウドカ : パーソントリップ チョウサ データ ト モバイル クウカン トウケイ ノ トウゴウ ホウホウ ニ ツイテ
  • Integration method for person trip survey data and mobile spatial statistics
  • パーソントリップ調査データとモバイル空間統計の統合方法について

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<p> In recent years, demographics regarding the spatiotemporal distribution of people in urban areas estimated from location information of mobile phone users are available for various usage. However, the available information of mobile phone demographic data is limited due to various reasons.</p><p> In order to address this limitation, we firstly constructed a model to estimate the spatiotemporal distribution of people by considering the differences of population density according to the location, time, and uses of building where they are. Next, we complemented their attribute and purpose of stay for each grid cell, by incorporating the information of Person Trip survey data (PT data). Finally, using the advanced spatiotemporal demographic data, we discussed the spatiotemporal distribution of people, which varies according to areas, date, and time. Summary and conclusions are as follows.</p><p> (1) First, we proposed a model to estimate the number of people being inside/outside of buildings for each building use in detailed units of space and time, by using Mobile Spatial Statistics (MSS) data and Building Point data, which is the GIS data including detailed attribute information such as floor area and building use.</p><p> (2) Next, we added the detailed attribute information to the people being inside/outside of buildings for each building use, by using the information of Person Trip survey data (PT data) in which detailed personal attributes as well as the location and time information of the departure and arrival, purpose of trip, and means of transportation are available. In this process, we utilized the fact that detailed personal attributes of people are deeply dependent on the location, time, and building use where they are staying.</p><p> (3) Finally, using the spatiotemporal distribution of people which was estimated by the proposed model, we attempted to making regional comparisons of the spatiotemporal distribution of people in urban areas.</p><p> In conclusion, we demonstrated that it is possible to grasp the spatiotemporal characteristics of population distribution attached with detailed attribute information such as their age, gender, occupation, and purpose of stay that vary according to the location, time, and building use where they are.</p>

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