Application of spatiotemporal big data technology in land spatial planning: Six application scenarios and four application stages

admin 1056 2021-09-08 10:20:47


Six application scenarios of spatio - temporal big data technology in land spatial planning
At present, spatiotemporal big data technology has been widely used in the fields of population scale and distribution, occupational and residential spatial relationship, public center system, facility service scope, urban spatial correlation, urban activities and block vitality in land and space planning. Relying on spatio - temporal big data technology can break through the limitations of traditional methods and respond to the application needs of the above planning issues with relatively higher efficiency and lower cost.
1. For the application scenarios of population size and distribution, spatio - temporal big data can detect population information that was not available in the past
Relying on mobile signaling or Internet positioning data, the identification and statistics of various urban population sizes and population spatial distribution can be realized. In addition to identifying the urban resident population, spatio - temporal big data technology can also effectively identify the urban actual service population and short - term resident population, realize the estimation of various population sizes and the exploration of various population distribution, break through the limitations of traditional survey methods in population survey statistics, and greatly expand the ability to understand the change trend of urban population size .
Compared with the traditional survey methods, the population statistics relying on spatio - temporal big data technology can freely define the statistical boundary and calculation period, realize the estimation of urban population scale and population distribution of each unit in the city at a relatively lower cost and higher efficiency, effectively assist in the calculation of all kinds of urban population, and find the rapidly changing areas of urban population distribution. At the same time, through the comparison and verification between the big data statistical population and the traditional survey population, we can more accurately grasp the change trend of the permanent population of each city in the interval between the national census every ten years.
2. For the application scenarios of spatial relationship between work and residence, spatio - temporal big data can reveal the relationship between work and residence at different spatial scales
The use of spatio - temporal big data technology can also reveal the working and living relationship in urban and regional space. This technical means is not only suitable for the analysis of working and living space within the city, but also can be used to reveal the cross city commuting relationship within the region. By measuring the day and night residence behavior of mobile device users in the research period, judge the employment place and residence of device users, so as to build the OD connection of commuting flow and intuitively reflect the scale and flow direction of commuting flow.
The advantages of spatiotemporal big data technology are reflected in the wide range of data collection space, large sample size and relatively simple data processing, which is convenient to measure the spatial relationship between work and residence caused by cross city commuting behavior . By changing the statistical spatial unit, spatio - temporal big data can meet the analysis needs of different spatial levels. In addition, regular data collection and analysis can realize long - term effective monitoring of the same kind of activities and reveal the evolution dynamics of the relationship between work and residence in the metropolitan area.
3. For the application scenarios of the public center system, spatio - temporal big data can reveal the level and service scope of the public center
Through spatio - temporal big data technology, identify the spatial activity behavior characteristics of mobile device users other than their residence and employment behavior, that is, their visit and stay place, which can be applied to the analysis of the public center system. At present, the use of spatio - temporal big data can clearly identify the residence, recreation and other places of daily life of the people visiting the public center, and count the source and scale of the visitor population in each public center, so as to provide support for the planning of urban public center.
In the analysis case of Shanghai public center system, combined with the data of two years, this paper further discusses the evolution characteristics of Shanghai public center system by comparing the changes of the number of visitors in each public center. Spatiotemporal big data widely covers the visitors to the public center. From the perspective of all visitors, it interprets the service scale, source and function of the public center, making up for the limitations of traditional methods of investigation and analysis. At the same time, using the accumulation of multi - year spatio - temporal big data, the evolution characteristics of the public center are mined through the changes of the service characteristics of the public center in different years, and then provide decision support for the planning of the public center system.
4. For the application scenarios of the facility service scope, spatio - temporal big data technology can reveal the main service scope of the facility
Following the above analysis ideas of urban public center system, the research on the service scope of facilities is essentially to focus the analysis object on the object with relatively smaller scale and explore the main service scope of facilities. Relying on spatio - temporal big data technology, identify the source and destination of facility users, reveal the main service scope of facilities from a spatial perspective, and further compare the differences in service characteristics between similar facilities.
On the basis of combining the nature, location, scale and grade of facilities, it can reveal the relationship between facilities and the daily life of urban residents, so as to judge the actual service efficiency and service level characteristics of facilities. Spatiotemporal big data technology can be widely applied to the analysis of various facilities, including but not limited to airports, high - speed railway stations, parks, subway stations and other facility objects. Its information collection has large scale, wide range and high real - time data. Through the periodic change analysis of facility service scope, it can also reveal the dynamic service characteristics of research facilities, so as to effectively support the needs of facility service evaluation, facility service demand prediction, facility planning and design, etc.
5. For the application scenario of urban spatial association, spatio - temporal big data technology can reflect the flow and connection characteristics between cities
From the regional level, the inter city personnel factor flow reflected by spatio - temporal big data can reveal the connection characteristics of inter city mobility in the region, so as to more intuitively represent the personnel connection and even functional connection between cities. Relying on the departure and destination of users' cross city mobility identified by spatio - temporal big data, we can measure the inflow and outflow scale between cities and reveal the characteristics of personnel mobility between cities.
In addition, by identifying the scale and flow direction of intercity personnel flow within a certain region, the spatial system of urban agglomeration can also be effectively measured. By identifying the spatial high - value agglomeration range of large - scale personnel mobility in a city, measure the metropolitan area from the perspective of inter city mobility linkages. Spatiotemporal big data technology can obtain as many personnel flow trajectories as possible in a wider range, greatly reduce the cost of data acquisition, significantly improve the scale of research objects, efficiently obtain intercity personnel flow data in different characteristic time periods, and provide guarantee for a more accurate understanding of the spatial connection characteristics of urban areas.
6. For the application scenarios of urban activities and neighborhood vitality, spatio - temporal big data technology can describe urban vitality
From the urban and block levels, relying on spatio - temporal big data, it can be used to explore the relationship between urban residents' activity intensity and space, and can effectively identify personnel activities and their dynamic characteristics at different scales such as urban and street space in each characteristic period, so as to describe the vitality of urban and street space.
The traditional view holds that the built - up environment of the city has an important impact on the vitality of the city and the streets. The influence of built - up environmental factors such as street scale, building density and negative boundary on the vitality of the city can be revealed by means of spatio - temporal big data. At the same time, spatiotemporal big data technology can be used to test and explore planning and design theory. Taking Nanjing West Road in Shanghai as an example, it is found that the spatial vitality of urban streets is significantly affected by built environment factors such as street length, sidewalk width and commercial interface continuity. The endogenous relationship between urban spatial Street vitality and built environment is discussed.
Application of spatiotemporal big data technology in various stages of land spatial planning
In the whole cycle process of land spatial planning, spatio - temporal big data technology can play an effective role in multiple planning stages. At present, it can effectively embed the four planning stages of basic investigation, current situation analysis, real - time monitoring and modeling prediction. The spatio - temporal big data technology effectively solves the problems that are difficult to be solved by the traditional methods of land spatial planning. In the four stages, the development of spatio - temporal big data technology application technology is different.
1. Basic survey: estimate the actual service population, population change scale and population distribution characteristics of the city
The early introduction of spatio - temporal big data technology in the planning industry began with basic investigation. In fact, the application of spatio - temporal big data technology is not to replace the traditional investigation methods, but to find a breakthrough in the content that is difficult to investigate based on conventional methods, so as to tap the advantages of spatio - temporal big data technology. The breakthrough of this new technology is especially reflected in the investigation of urban population scale and spatial distribution.
Measuring urban population size and spatial distribution through spatio - temporal big data can make up for the actual service population and short - term resident population that cannot be measured by conventional surveys, and help to guide resource allocation and service facility layout more reasonably and efficiently. The actual service population and short - term resident population are the population survey needs generated in response to the planning needs. At present, both types of population indicators have been included in the planning evaluation and monitoring index system of the Ministry of natural resources. Generally speaking, the actual service population of a city is the sum of the short - term resident population and the permanent resident population. Using spatiotemporal big data technology can effectively identify the short - term resident population and its spatial distribution characteristics of the city. Taking the population survey of Wuhan for two consecutive years from 2019 to 2020 as an example, the ratio of short - term resident population to permanent population in Wuhan is about 3.5% - 6%, with an average of 4.91%, that is, the actual service population in Wuhan is about 1.05 times that of permanent population.
2. Current situation analysis: reveal the actual service scope of the facility and its impact on the surrounding environment and facilities
Relying on spatiotemporal big data technology for current situation analysis and evaluation is a mature and widely recognized application practice means. Spatiotemporal big data technology aims to supplement the shortcomings of traditional methods, explore ways that are difficult to achieve by conventional status analysis methods, and obtain more perfect analysis results.
Taking the analysis of the current service characteristics of Shanghai country parks as an example, this paper uses mobile phone signaling data to reveal the temporal and spatial behavior characteristics of visitors to Shanghai country parks. This paper evaluates the opened Shanghai country park from three aspects: the relationship between the park and the city, the relationship between the park and the surrounding villages and towns, and the internal functional streamline of the park, and puts forward suggestions for the planning and design of Shanghai country park. The main breakthrough of spatio - temporal big data technology is that the residence of tourists is calculated through the spatio - temporal characteristics of tourists' activities, and the actual service scope of the park is obtained. Secondly, through the proportion of tourists staying at different locations in the country park, we can get the tourist moving line in the park. Thirdly, the development linkage between the country park and the surrounding villages and towns is evaluated by measuring the activities of country park tourists visiting the surrounding areas of the park on the same day. This answers three clear questions about where tourists from Shanghai country parks come from, how to visit in the park, and where they go after leaving.
3. Implementation monitoring: dynamically track the personnel activity intensity of urban construction land and identify construction land with low activity intensity
Spatiotemporal big data has the prominent feature of high - frequency update, which makes it possible to be used for planning, implementation and monitoring. Considering that all parts of the country are widely exploring the integration of spatio - temporal big data technology into the "one map" implementation supervision system of land and space planning, it is of great significance to mine spatio - temporal big data technology to serve the monitoring of planning implementation. In practice, spatio - temporal big data technology has been effectively applied to monitor the use efficiency of construction land and identify construction land with low activity intensity.
Relying on spatio - temporal big data, it can evaluate the actual activity efficiency of personnel in the existing construction land based on human activities, so as to realize the comprehensive monitoring of people and land. The traditional construction land monitoring based on remote sensing image is a monitoring method based on the perception of "land" and "object". The monitoring of construction land based on spatio - temporal big data is from the perspective of human activities, and then compared with the existing construction land identified by remote sensing images, so as to realize the integration of "human" and "land" perception. This method is especially suitable for monitoring the planning practice of mega cities and big cities. It can efficiently, quickly and comprehensively monitor the personnel activity intensity on the construction land in the city and identify the construction land with relatively low activity intensity. This monitoring method can quickly and widely monitor the activity intensity of personnel on the construction land in the city, and quickly identify the construction land with low activity intensity. Therefore, it is suitable to integrate into the national and provincial land spatial planning implementation monitoring platform, such as the "provincial one map implementation supervision system".
4. Modeling and prediction: the combination of spatiotemporal big data and urban model can predict urban development
The planning responsibility is to deal with the future, and whether spatio - temporal big data can be used to predict the future development trend, which is also the technological frontier explored in recent years. Building models through urban simulation is the most commonly used planning and forecasting technology. The combination of spatio - temporal big data technology and urban model method can serve the needs of modeling and prediction. Combine spatiotemporal big data with modeling methods, calibrate the model with spatiotemporal big data, and predict the future with the model“ "Big data + urban simulation" is a more feasible prediction method for predicting the future development of cities.
The introduction of spatio - temporal big data technology can verify the modeling model, so as to obtain the model verified by big data observation data, so that it can be more reliably used for simulation and prediction. Taking the prediction of the sphere of influence in the central urban areas of four cities in the "Nanchang Jiujiang" region as an example, the mobile phone signaling data is used to observe the urban sphere of influence, the parameter selection is corrected in combination with huff model, and the change trend of the sphere of influence in the central urban areas of four cities in the region is simulated on the premise that the existing regional traffic system remains unchanged, Realize the prediction of future development trend. In the case, the flow data between the city and the surrounding areas observed by relying on spatio - temporal big data technology is not only the data basis to intuitively reflect the relationship between the urban hinterland, but also an important link to improve the urban simulation and prediction, which effectively supports the model verification in urban simulation. At present, the prediction approach of "spatio - temporal big data + urban model" is the technical frontier of spatio - temporal big data for planning prediction, but it still stays in academic research and has not entered the field of planning practice.
Summary
At present, spatio - temporal big data technology can play an effective role in six areas: urban population scale, spatial relationship between work and residence, public center system, facility service level, regional urban connection and urban activity vitality. At the same time, spatio - temporal big data technology can be effectively embedded in the whole cycle management process of land spatial planning. The effective application practice in the four stages of basic investigation, current situation analysis, modeling and prediction, monitoring and evaluation can realize the issues that are difficult to be solved by traditional technologies and methods.
Generally speaking, in the two stages of basic investigation and current situation analysis, spatio - temporal big data technology has shown great application value and wide application potential. Starting with the actual service population survey and short - term resident population survey, and relying on the continuous exploration of individual behavior trajectory, we can obtain more information about crowd activities. On this basis, the combination of human space - time behavior and urban space can effectively interpret the current characteristics of urban space. The spatio - temporal big data technology has also started practical application in the monitoring and evaluation stage. By combining people's spatio - temporal behavior with land and other information, it can track and detect the activity intensity of personnel on urban construction land and identify construction land with low activity intensity. For the modeling and prediction stage, the application of spatio - temporal big data technology still has a long way to go and is still under exploration. The combination of spatio - temporal big data and urban modeling should be a feasible technical way for spatio - temporal big data to be used in the modeling and prediction stage.

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