Simulation of spatial population dynamics based on labor economics and multi-agent systems: a case study on a rapidly developing manufacturing metropolis

Spatial population dynamics affects resource allocation in urban planning. Simulation of population dynamics can provide useful information to urban planning for rapidly developing manufacturing metropolises. In such a metropolis with a concentration of immigrant labor forces, individual employment choices could have a significant effect on their residential decisions. There remains a need for an efficient method, which can simulate spatial population dynamics by considering the interactions between employment and residential choices. This article proposes an agent-based model for simulation of spatial population dynamics by addressing the influence of labor market on individual residential decisions. Labor economics theory is incorporated into a multi-agent system in this model. The long-term equilibrium process of labor market is established to define the interactions between labor supply and labor demand. An agent-based approach is adopted to simulate the economic behaviors and residential decisions of population individuals. The residential decisions of individuals would eventually have consequences on spatial population dynamics. The proposed model has been verified by the spatial dynamics simulation (2007 to 2010) of Dongguan, an emerging and renowned manufacturing metropolis in the Pearl River Delta, China. The results indicate that the simulated population size and spatial distribution of each town in Dongguan are close to those obtained from census data. The proposed model is also applied to predict spatial population dynamics based on two economic planning scenarios in Dongguan from 2010 to2015. The predicted results provide insights into the population dynamics of this fast-growing region.     

A spatial dynamic model of population changes in a vulnerable coastal environment

This study developed a spatial dynamic model to examine the coupled natural–human responses in the form of changes in population and associated developed land area in the Lower Mississippi River Basin region. The goal was to identify key socioeconomic factors (utility) and environmental factors (hazard damages, elevation, and subsidence rate) that affected population changes, as well as to examine how population changes affected the local utility and the local environment reciprocally. We first applied areal interpolation techniques with the volume-preserving property to transform all the data at Year 2000 into a unified 3 km by 3 km cellular space. We then built an Elastic Net model to extract 12 variables from a set of 33 for the spatial dynamic model. Afterward, we calibrated the neighborhood effects with a genetic algorithm and use the spatial dynamic model to simulate population and developed land area in 2010. Furthermore, we took a Monte Carlo approach for analyzing the uncertainty of the model outcome. Our accuracy assessment shows that the model on average slightly overpredicts the number of population and the developed land percentage at 2010, as indicated by the low values of mean absolute deviation (MAD) due to quantity. On the other hand, the MADs due to allocation are larger than the MADs due to quantity, with most outliers found in the New Orleans region where population and urban development declined significantly during 2000–2010 after Hurricane Katrina. The proposed model sheds light on the complex relationships between coastal hazards and human responses and provides useful insights to strategic development for coastal sustainability.     

Disaggregating the historical demographic sources of regional foreign-born and native-born population growth in the United States: a new method with applications

This paper analyzes the demographic processes that contribute to population growth and redistribution in a multiregional system using a new method. The method incorporates a historical perspective that can be used to trace dynamic population processes as they evolve over time. It uses an open multiregional projection model framework in identifying the contributions to regional growth made by each of the principal demographic components of change: fertility, mortality, immigration, emigration, in-migration, and out migration. At the same time, the method recognizes the importance of disaggregating the native-born and foreign-born populations. Available public data and indirect estimation techniques are used to develop the data inputs for the projection model, with which the regional population changes for each 5-year period between 1950 and 1990 were reconstructed. Regional growth rates for the foreign-born and native-born populations are partitioned into the separate demographic components of change, and the projection model identifies the separate contributions to regional growth made by each population. This allows a direct comparison of the impact of immigration with those of corresponding native-born contributions effected through internal migration and natural increases. Finally, the application of the method allows the identification of the contribution that 'recent' (post-1965) immigrant cohorts have made to the composition of the youngest age groups in each region, and also to simulate the impacts of zero immigration scenarios on regional growth.     

A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model

Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder?Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model.     

多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用

对统计型人口数据进行格网形式的空间化可更直观地展示人口的空间分布,但不同的人口空间化建模方法和不同的格网尺度在表达人口空间化结果方面存在差异。本文在人口特征分区的基础上,引入DMSP/OLS夜间灯光对城镇用地进行再分类,采用多元统计回归和地理加权回归方法(GWR),开展人口统计数据空间化多尺度模型研究,生成1 km、5 km和10 km等3个尺度的2010年安徽省人口空间数据,并对3个尺度下2个模型结果进行精度评价与比较。结果表明：人口空间数据精度不仅与建模所用方法关系密切,还受到建模格网尺度大小的影响。基于多元统计回归方法的模型估计人口数与实际人口的平均相对误差值随着尺度的增加而降低,而基于GWR方法获得的人口空间数据误差值随着尺度的增加而升高。整体来看,基于GWR方法的1 km研究尺度的人口空间数据平均相对误差最低(22.31%)。区域地形地貌条件与人口空间数据误差有较强的关联,地貌类型复杂的山区人口空间数据误差较大。     

Fine-grained prediction of urban population using mobile phone location data

Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.     

Use of the population potential model for predicting the growth of cities in a region (with reference to Moscow Oblast)

"A modification of the population potential model is used to analyze the structure of the potential of population for cities of the Moscow region, to develop a relationship between population potential and city size, and to use this procedure to forecast future population change in Moscow Oblast cities." Cities in the region with a population of 50,000 or more at the censuses of 1959, 1970, and 1979 are included.     

Using geographical information system and satellite imagery within a numerical simulation of regional urban growth

This paper represents an attempt to combine a geographical information system (GIS) and satellite imagery within a numerical model for the simulation of regional urban growth. The area under study is located in the southeast of France, between the cities of La Ciotat and Toulon. It covers 1600 km². The spatial resolution of the GIS as well as of the remote sensing data is better than one hectare (80 × 80 m²). Two Landsat MSS satellite images were analysed by a supervised maximum likelihood classifier to provide two maps of land use and urban domain for 1976 and 1981. A numerical model for the growth of the density of the population is applied to the urban domain. It takes into account some constraints that are due to the environment as well as the changes of population per city and per year. Seven environmental parameters are defined by pixel and originate from the content of the database, namely, land use, topography, road network, forest properties and forest fire characteristics. The global change in population is given by observations for the first years and by scenarios for the predicted years. The numerical simulation gives maps of the urban domain per year and per scenario. The initial state (year 1976) and the simulated states Tor the years 2000 and 2025 are compared and the differences are discussed briefly.     

上海市常住人口增长趋势研究院基于劳动年龄人口的情景模拟

国内外大城市的快速发展很大程度上依赖于劳动力的充分供给,制定合适的人口发展战略及政策至关重要。本文基于队列要素法,以劳动年龄人口规模作为重要变量对上海市人口规模进行情景模拟,依据结果对上海市未来常住人口发展趋势进行判断。结果显示,外来劳动年龄人口对上海市未来发展有重要支撑作用,上海应该给予外来迁入人口更多的关注和肯定,而不是盲目地限制;同时,判定上海市2040年常住人口规模将增长到3000万～3870万之间。本方法可以广泛应用于与上海具有相似人口年龄结构和人口规模变化趋势的大型城市和特大型城市,对中小型城市的人口规模模拟和政策制定也具有一定的参考意义。     

基于空间相互作用模型的商业布局——以北京市海淀区为例

在分析北京市海淀区人口增长与商业发展的基础上,运用空间相互作用理论和模型, 定量地测算了海淀区各个街乡的人口潜能与商业吸引力,对该区的人口与商业分布状况及其空间互动关系进行了分析,并根据人口增长的组合预测结果对未来海淀区各个街乡商业配置进行了具体的测算,在为商业发展布局规划提供决策支持的同时,探讨一种城市商业活动的空间分析框架。     

Migration as a phenomenon and process of population change

This paper relates migration with population growth and economic development. Migration affects both production and consumption; it should therefore be considered at the household-family level and analyzed within the utility maximization model. 1 determinant of internal migration is population pressure on land. Migration in the agricultural sector can provide resources to be reinvested in physical capital for agricultural technological change. Usually the agricultural family sends the maturing son to the city because of his better skills and education than other family members. Migrants are among the first to obtain newly created jobs or displace less qualified workers from existing jobs. Thus, migration initially causes disequilibrium, but since it supports technological improvement on the farms, it becomes an equilibriating mechanism. Migration rates are highest in villages where land is most unequally divided. Some of the main noneconomic determinants of migration include social conflicts, religious outbursts and movements, violence, wars, pestilence, epidemics, caste conflicts, political and ideological differences, and climatic unsuitability. Migration and fertility need to be analyzed at the family level rather than at the societal or micro level.     

黑龙江省内部中长期城镇人口迁移行为分析与建模

人口迁移的时空分异与影响机制一直是人口学研究关注的焦点。本文基于社保关系转移视角,从时间和空间两个维度探究黑龙江省城镇工作人口省内的中长期迁移规律与时空分异特征,并且结合人口推拉理论与OD模型,综合考虑经济和就业政策因素,建立了能够反映以社保劳动关系转移为基础的中长期人口迁移动力学模型。研究结果表明：基于社保关系的城镇工作人口迁移符合截断指数幂律分布,呈现列维飞行特点;该省省内人口迁出呈现多层级跨区域分布格局,人口迁入呈现“四核心-环东南”格局,人口迁入与迁出空间格局具有一定程度的类似性;黑龙江省边境城市人口流失情况较为严重;城镇职工迁移行为的无序性不断加大,迁移的频率较高;但是经过通过模拟,该省城镇职工的迁移系统趋向于平衡发展。     

长三角城际日常人口移动网络的格局与影响机制

人口移动是城际要素流动和资源配置的主要载体,是建构区域网络组织的主要形式。本研究关注人口移动的重要组分——短时间尺度48小时内城际日常移动（daily intercity mobility）,基于微博签到大数据,通过分析长三角人口日常移动的城际格局发现,人口流动具有向多核心极化的分布特征,受到省级行政边界阻碍,并存在明显的省际差异。通过重力模型进行反向验证,对影响因素进行系统统计检验发现,移动的时间等成本和产业结构、文化、教育等城市特征补充塑造了由物理距离决定的城际移动模式,且相比于移动成本,城市特征对人口移动有着更大的影响。本文拓展了重力模型检验网络影响机制的方法实践,解析了城际日常移动机理,并从人口日常移动角度为理解长三角一体化的进程与优化策略提供参考。     

基于GIS的大连城市人口空间分布变动及模拟研究

利用GIS手段对四普、五普数据进行处理,深入剖析了20世纪90年代大连城市人口空间分布变动形式。1990年代,大连人口分布变动模式为集聚中有扩散,以集聚为主,仍处于城市化阶段。1990-2000年,中心城区、近郊区人口绝对数量增多、密度增大,远郊区人口绝对数量减少,密度下降：人口正在向中心城区的“中间地带”集聚;人口重心向西南方向移动,位移较大;从人口集中指数来看．大连市分布很均匀：通过对1990年和2000年大连市中心城区人口密度进行模拟,二次函数Quadrafte：T=b0＋b1r＋b2r2为最优模型,该模型较好地反应了大连市中心城区人口密度分布趋势。     

Soil erosion risk evaluation using GIS in the Yuanmou County, a dry-hot valley of Yunnan, China

Soil erosion is a major threat to sustainable agriculture. Evaluating regional erosion risk is increasingly needed by national and in-ternational environmental agencies. This study elaborates a model (using spatial principal component analysis [SPCA]) method for the evaluation of soil erosion risk in a representative area of dry-hot valley (Yuanmou County) at a scale of 1:100,000 using a spatial database and GIS. The model contains seven factors: elevation, slope, annual precipitation, land use, vegetation, soil, and population density. The evaluation results show that five grades of soil erosion risk: very low, low, medium, high, and very high. These are divided in the study area, and a soil erosion risk evaluation map is created. The model may be applicable to other areas of China because it utilizes spatial data that are generally available.     

Density of rural population in an Indian situation: a contemporary approach

The spatial patterns in the density of rural population in an area are a function of the complex interplay of physical, economic, social, and technological forces. However, natural forces play a more prominent role than others in this respect. This is especially true of the developing world where application of modern technologies is limited and economies are predominantly agricultural in nature. This paper establishes quantitatively the superiority of the physical environment in effecting spatial patterns in the density of the rural population in Punjab. Only 3 variables taken from natural setup, rainfall, soil, and water, jointly explain over 53% of the spatial variance in the density of the rural population in Punjab. This clearly proves the supremacy of physical forces over others in determining spatial patterns in the density of the rural population in an agricultural area of the developing world. However, the concept needs further testing in some similar areas to establish its validity on a wider scale.     

Towards decentralization: Spatial changes of employment and population in Tehran Metropolitan Region,Iran

Employment and population spatial structure of metropolitan regions have evolved in the era of post-industrialization. Empirical and theoretical debates have consensus that monocentric model is no longer explaining the phenomenon of population and employment distribution. One argument regards polycentricity as a permanent spatial structure beyond monocentricity; another idea believes that dispersion can explain spatial variation of employment and population too. This study investigated the level of monocentricity and primacy of principal city in Tehran Metropolitan Region (TMR), in addition to sub-centering and level of polycentrism. Dispersion of employment and population in TMR was also analyzed. The results revealed that standard monocentric model is not able to explain evolution of Tehran although it is dominant core of the region. The polycentric models illustrated that although the changes from 2006 to 2011 were not considerable, employment was more concentrated than population; they also showed that there is no distinction between polycentric and dispersion and regions may have some levels of polycentricity and dispersion.     

基于GWR模型的中国水资源绿色效率驱动机理

遵循“以人文本”的绿色发展理念,综合考虑水资源利用的经济、社会及环境效益,运用地理加权回归模型,综合选取自然、社会经济、环境、科技四大因素共19个指标,测度2000—2016年中国水资源绿色效率,并对其驱动机理进行探究。结果表明：中国水资源绿色效率存在着显著的空间正相关性和空间分布不均衡性;各因素对水资源绿色效率的影响存在着空间差异,且随着时间而变化;总体来看,产业结构、工农业发展水平、交通基础设施建设、环保投入和科技转化率等因素对中国水资源绿色效率的提高具有显著影响,人均水资源量、人口规模和污染物排放等因素的增加则限制了水资源绿色效率的提高;就其空间差异而言,水资源禀赋、人口规模等因素对西部地区水资源绿色效率的促进作用大于东部,而产业结构、用水结构、交通基础设施建设等因素对水资源绿色效率的影响程度东部大于西部。研究结果对于解决中国水资源短缺问题,因地制宜地制定适合各地区发展的水资源政策,具有重要的实际意义。     

The Spatial Restructuring of Population in Metropolitan Beijing: Toward Polycentricity in the Post-Reform ERA

Based on data from the third (1982), fourth (1990), and fifth (2000) Population Census of China, this research examines the changing patterns of population distribution in the Beijing metropolitan area in the post-reform era. In the mid-1980s, China launched a series of urban land use reforms aimed toward achieving a market economy. A direct impact of these reforms in Beijing was the restructuring of land uses, evidenced by the relocation of central-city residents to the suburbs to make room for commercial development. This residential suburbanization trend emerged during the 1980s and accelerated in the 1990s. Population change rates varied significantly across subdistricts, and variation was much greater in the 1990s than in the 1980s, indicating an increasingly diverse set of growth trajectories across metropolitan Beijing over time. Various population density functions are tested. The population density pattern is best characterized by a monocentric model in 1982, a dual-centered model in 1990, and a seven-centered model in 2000. This transition in Beijing's urban form toward polycentricity is similar to trends in most Western cities.     

Measuring Potential Access to Primary Healthcare Services: The Influence of Alternative Spatial Representations of Population

Spatial accessibility measures are an important policy tool for managing healthcare provision and reducing health inequality. The two‐step floating catchment area technique, in common with many alternative methodologies, requires that demand‐side population be estimated using spatial interpolation techniques. This article studies the implications of adopting differing spatial representations of population on healthcare accessibility modeling outcomes. Results indicate that a dasymetric model yields lower accessibility scores than a standard pro rata model. More important, the difference is spatially disproportionate, suggesting that the degree of disadvantage experienced in rural areas may be greater than has previously been recognized.