Exploring Urban Population Forecasting and Spatial Distribution Modeling with Artificial Intelligence Technology
06 May 2019-Cmes-computer Modeling in Engineering & Sciences (Computers, Materials and Continua (Tech Science Press))-Vol. 119, Iss: 2, pp 295-310
TL;DR: Wang et al. as mentioned in this paper developed a new method based on the fade factor and the slide window to improve the precision of small area population forecasting and improved the spatial resolution of urban population distribution model.
Abstract: The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology, city planning and Geo-Informatics. However, the two problems need to be solved for providing the high precision population information. One is how to improve the population forecasting precision of small area (e.g., street scale); another is how to improve the spatial resolution of urban population distribution model. To solve the two problems, some new methods are proposed in this contribution. (1) To improve the precision of small area population forecasting, a new method is developed based on the fade factor and the slide window. (2) To improve the spatial resolution of urban population distribution model, a new method is proposed based on the land classification, public facility information and the artificial intelligence technology. For validation of the proposed methods, the real population data of 15 streets in Xicheng district, Beijing, China from 2010 to 2016, the remote sensing images and the public facility data are collected and used. A number of experiments are performed. The results show that the spatial resolution of proposed model reaches 30m*30m and the forecasting precision is better than 5% using the proposed method to forecast the population of 15 streets in Xicheng district in the next four years.
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TL;DR: Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.
Abstract: Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.
194 citations
TL;DR: In this article, the authors analyzed the temporal and spatial patterns and regional differences in the production-living-ecological space (PLES) in the middle reaches of the Yangtze River.
Abstract: The urban agglomeration in the middle reaches of the Yangtze River, which is the second largest urban agglomeration in China, represents a typical land space range of ecological vulnerability in China. Large differences occur in economic development mode between resource- and non-resource-based cities in this basin area. Accurate identification of the evolution and regional differences in the production-living-ecological space (PLES) is very important in order to elucidate the development and utilization of land space in the region. At present, relevant research has largely focused on the classification and determination of PLES temporal and spatial patterns. Temporal and spatial pattern research has mainly considered a single scale of administrative division, whereas fewer studies have analyzed the temporal and spatial patterns and regional differences in the PLES in ecologically fragile natural watersheds. Therefore, based on PLES classification, the regional differences in the PLES between two types of cities in the basin are measured via the Theil index and exploratory spatial data analysis (ESDA). First, the ecological space (ES) of these two types of cities in the urban agglomeration in the middle reaches of the Yangtze River is compressed by the production space (PS) and living space (LS), in which the ES of resource-based cities is compressed for a longer period, and the phenomenon involving PS compression by the LS and ES mainly occurs in non-resource-based cities within the urban agglomeration in the middle reaches of the Yangtze River. Second, the PLES of these two types of cities exhibits the characteristics of spatial aggregation, and high- and low-density areas of the PLES remain relatively stable. Third, the regional differences in the PLES of the urban agglomeration in the middle reaches of the Yangtze River mainly originate from intraregional differences. The PLES of these two types of cities in the urban agglomeration in the middle reaches of the Yangtze River is more sensitive to changes in economic development than to those in the population distribution.
14 citations
TL;DR: In this paper , the authors present a comprehensive review of the areas of urban planning in which AI technologies are contemplated or applied, and it is analyzed how AI technologies support or could potentially support smart and sustainable development.
9 citations
TL;DR: In this article, a macro-micro joint decision model was proposed to improve the ability of urban space evolution simulation, and the simulation objects were unified into production, living and ecological space to realize multiple planning in one.
Abstract: The precise simulation of urban space evolution and grasping of the leading factors are the most important basis for urban space planning. However, the simulation ability of current models is lacking when it comes to complicated/unpredictable urban space changes, resulting in flawed government decision-making and wasting of urban resources. In this study, a macro-micro joint decision model was proposed to improve the ability of urban space evolution simulation. The simulation objects were unified into production, living and ecological space to realize "multiple planning in one". For validation of the proposed model and method, remote sensing images, geographic information and socio-economic data of Xuzhou, China from 2000 to 2020 were collected and tested. The results showed that the simulation precision of the cellular automata (CA) model was about 87% (Kappa coefficient), which improved to 89% if using a CA and multi-agent system (MAS) joint model. The simulation precision could be better than 92% using the prosed model. The result of factor weight determination indicated that the micro factors affected the evolution of production and living space more than the macro factors, while the macro factors had more influence on the evolution of ecological space than the micro factors. Therefore, active policies should be formulated to strengthen the ideological guidance towards micro individuals (e.g., a resident, farmer, or entrepreneur), and avoid disordered development of living and production space. In addition, ecological space planning should closely link with the local environment and natural conditions, to improve urban ecological carrying capacity and realize urban sustainable development.
5 citations
TL;DR: Zhang et al. as discussed by the authors proposed an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB) for creating multiple candidates while using fewer computational resources.
Abstract: Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account; it is used to identify the ELMs that have good diversity and low error. In addition, we propose an ECDPB with powerful optimizing ability; it is employed to find the optimal subset of ELMs. The selected ELMs can then be used to form an ensemble classifier. Experiments on 10 benchmark datasets have been conducted, and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.
1 citations
References
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01 Jul 1951
870 citations
"Exploring Urban Population Forecast..." refers background or methods in this paper
...Currently, there are three kinds of urban population spatial models: a) population density model [Clark (1951); Tanner (1961); Smeed (1961); Anderson (1985)]; b) spatial interpolation model [Tober (1979); Lam (1983)]; c) geographical factor model [Harvey (2002); Tian, Chen, Yue et al. (2004); Xu,…...
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...Therefore, many scholars have investigated different methods to urban population forecasting and spatial distribution [Clark (1951); Wu and Murray (2005); Wilson (2015); Zou, Zhang and Wang (2018)]....
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...This kind of population spatial distribution was described by Clark, see Clark [Clark (1951)]....
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TL;DR: In this article, the robustness of these techniques as a practical methodology for population estimation was investigated and evaluated using a primary image for model development and training, and a second image for validation.
Abstract: Small-area population densities and counts were estimated for Australian census collection districts (CDs), using Landsat TM imagery. A number of mathematical and statistical refinements to previously reported methods were explored. The robustness of these techniques as a practical methodology for population estimation was investigated and evaluated using a primary image for model development and training, and a second image for validation. Correlations of up to 0.92 in the training set and up to 0.86 in the validation set were obtained between census and remote sensing estimates of CD population density, with median proportional errors of 17.4% and 18.4%, respectively. Total urban populations were estimated with errors of +1% and-3%, respectively. These results indicate a moderate level of accuracy and a substantial degree of robustness. Accuracy was greatest in suburban areas of intermediate population density. There was a general tendency towards attenuation in all models tested, with high densities be...
165 citations
Additional excerpts
...…spatial models: a) population density model [Clark (1951); Tanner (1961); Smeed (1961); Anderson (1985)]; b) spatial interpolation model [Tober (1979); Lam (1983)]; c) geographical factor model [Harvey (2002); Tian, Chen, Yue et al. (2004); Xu, Mei and Han (1994); Zhuo, Chen, Shi et al. (2005)]....
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TL;DR: In this study, impervious surface fraction derived from Thematic Mapper (TM) imagery was applied to derive the underlying population of an urban region and cokriging method was developed to interpolate population density by modeling the spatial correlation and cross-correlation of population and impervioussurface fraction.
102 citations
"Exploring Urban Population Forecast..." refers background in this paper
...Therefore, many scholars have investigated different methods to urban population forecasting and spatial distribution [Clark (1951); Wu and Murray (2005); Wilson (2015); Zou, Zhang and Wang (2018)]....
[...]
73 citations
"Exploring Urban Population Forecast..." refers background in this paper
...Currently, there are three kinds of urban population spatial models: a) population density model [Clark (1951); Tanner (1961); Smeed (1961); Anderson (1985)]; b) spatial interpolation model [Tober (1979); Lam (1983)]; c) geographical factor model [Harvey (2002); Tian, Chen, Yue et al. (2004); Xu,…...
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TL;DR: It is found that age-group error patterns are different for national projections than for subnational projections; that errors are substantially larger for some age groups than for others; that differences among age groups decline as the projection horizon becomes longer; and that differences in methodological complexity have no consistent impact on the precision and bias of age- group projections.
Abstract: A number of studies have evaluated the accuracy of projections of the size of the total population, but few have considered the accuracy of projections by age group. For many purposes, however, the relevant variable is the population of a particular age group, rather than the population as a whole. We investigated the precision and bias of a variety of age-group projections at the national and state levels in the United States and for counties in Florida. We also compared the accuracy of state and county projections that were derived from full-blown applications of the cohort-component method with the accuracy of projections that were derived from a simpler, less data-intensive version of the method. We found that age-group error patterns are different for national projections than for subnational projections; that errors are substantially larger for some age groups than for others; that differences in errors among age groups decline as the projection horizon becomes longer; and that differences in methodological complexity have no consistent impact on the precision and bias of age-group projections.
58 citations
"Exploring Urban Population Forecast..." refers methods in this paper
...One is the demographic model which is known as the “golden models”, such as double-region model, multiregion model, queue group element model, Hamilton-Perry model [Isserman (1993); Smith and Tayman (2003); Renski and Strate (2013)]....
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