Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the ...Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.展开更多
Grandpa Qin, now 74, has returned to where he had lived for more than six decades in the company of his daughter. In front of him, the old dilapidated scenes have all disappeared. The former nine 1.9-meter-wide lanes ...Grandpa Qin, now 74, has returned to where he had lived for more than six decades in the company of his daughter. In front of him, the old dilapidated scenes have all disappeared. The former nine 1.9-meter-wide lanes have become 12 six-meter-wide roads. The former crowded courtyards, 11 stairs down the lanes, have completely changed their outlook. The only things that seem to have not changed are the old Beijing style of blue tiles and gray bricks and the imperial city walls nearby that are the witnesses to all these changes. Grandpa Qin is among the 300 households who have returned after the rebuilding project was completed. Qin’s family lived in the Nanchizi community for five generations. The big展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.41571144)。
文摘Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.
文摘Grandpa Qin, now 74, has returned to where he had lived for more than six decades in the company of his daughter. In front of him, the old dilapidated scenes have all disappeared. The former nine 1.9-meter-wide lanes have become 12 six-meter-wide roads. The former crowded courtyards, 11 stairs down the lanes, have completely changed their outlook. The only things that seem to have not changed are the old Beijing style of blue tiles and gray bricks and the imperial city walls nearby that are the witnesses to all these changes. Grandpa Qin is among the 300 households who have returned after the rebuilding project was completed. Qin’s family lived in the Nanchizi community for five generations. The big
