An unconstrained statistical matching algorithm for combining individual and household level geo-specific census and survey data

The Population Census is an important source of statistical information in most countries that is capable of producing reliable estimates of population characteristics for small geographic areas. One limitation of a census is that there are many population characteristics that cannot be collected due to respondent burden or cost. This means that statistical agencies have to conduct population based surveys to provide social, economic and demographic characteristics for a target population which are not captured by a large-scale census. These surveys are usually capable of producing direct estimates at the national level and high level regions but often cannot produce reliable estimates for smaller areas. Due to the increasing demand for comprehensive statistical information not only at the national level but also for sub-national domains, there is a wide discussion in the literature about the use of statistical techniques that combine survey with census data to provide more detailed, finer-level estimates.Where censuses and sample surveys are based on the same reporting units, statistical matching techniques can be employed to link the records from survey and census data where exact matching of reporting units is impossible due to confidentiality restrictions. These techniques can then provide the detailed social, economic and demographic information required for small areas.An approach is developed in this paper in which a close-to-reality synthetic population of individuals and households is generated from available census tables using an iterative proportional updating (IPU) method. Statistical matching using a nearest neighbour method is then used to impute survey data to the individuals and households in the synthetic population. To evaluate this approach, 2011 Bangladesh census data is used to generate a district-specific synthetic population of individuals and households. Matching is then performed by imputing the nearest possible records among the 2011 Bangladesh Demographic and Health Survey to estimate the wealth index for each household within the synthetic population. The results show that using the method presented in this paper helps with achieving more representative estimates (comparing with direct survey estimates) particularly for areas with small sample sizes where not many population units with different socio-demographic characteristics are included.     

基于建筑物提取的精细尺度人口估算研究

以精细尺度的人口估算为目标,提出一种根据居民区建筑物属性估算人口数量的方法。首先基于Dempster-Shafer证据理论,结合LiDAR数据和高分辨率遥感影像进行建筑物的自动提取。根据土地利用分类图排除提取结果中的非居民区建筑后,按照线性回归的思想,通过对居民建筑物的数量、面积、体积等几何属性的优化选择建立人口估算模型。实验表明,利用该估算模型能够获得较高精度的小面积目标区域上的估算结果。该方法提高了人口估算的精细程度和自动化程度。     

The utility of built environment geospatial data for high-resolution dasymetric global population modeling

Several global gridded population data sets are available at unprecedented high-resolution, including recent releases at 100-m, 30-m, and 10-m resolution. These data sets are the result of the application of advanced methods to disaggregate census population counts from administrative units and facilitated by the proliferation of increasingly high-resolution spatial information pertaining to the built environment (e.g. built-up and building footprint data). Accordingly, these gridded population data are increasingly dependent on a single ancillary data set to inform the distribution of populations across space. Our study tests several combinations of binary masking variables (land areas, all building footprints, residential building footprints) and density variables (building footprint areas, building volumes) derived from characteristics of the built environment at 20× and 8000× downscaling using a flexible equation for high-resolution global dasymetric population modeling. The assessment is applied in New York City, where large spatial heterogeneities exist across confined geographic areas. Results confirm that the performance of the model generally improves as: (i) the binary masking variable becomes increasingly limiting; and, (ii) the density variable becomes more pronounced. However, application requires careful consideration due to their propensity to amplify both positive results and errors.     

Dynamics of Population Ageing in Nepal and the Need for Action

Over the past decades, the problem of ageing has grown steadily in Nepal. The demographic measures obtained from different population censuses of Nepal amply demonstrate this. In this context, the present study attempts to analyze population census 2011 data to assess elderly population across the nation by area types, eco-belts, development regions and by gender perspective through computation of major population ageing indicators for these cross-sectional areas of Nepal. The analysis showed that the indicators like median age, proportion of elderly population, elderly dependency ratio and life expectancy at birth have increased significantly in 2011 census compared to 2001 census stressing the need of appropriate actions for the management, welfare and care of elderly people in Nepal. Even though population ageing is found more evident in urban areas regarding median age and index of ageing, percent elderly population aged 60 and above and elderly dependency are actually higher in rural areas which can be attributed to more out-migration of young people in rural areas. Eco-belt-wise, elderly dependency is found higher in mountain but other ageing indicators are at higher levels in hills. Development region-wise, western region shows relatively more ageing stress compared to other regions except that median age is found highest in the central region. Additionally, two semi-log statistical models that fit well are also built to associate percent elderly populations of Nepal with crude birth rate based upon population censuses of Nepal.     

Mapping population change index in Southern Serbia (1961-2027) as a function of environmental factors

Niche analysis methods developed within the biogeography community are routinely used for species distribution modeling of wildlife and endangered species. So far, such techniques have not been used to explain distribution of people in an area, nor to assess spatio-temporal dynamics of human populations. In this paper, the MaxEnt approach to species distribution modeling and publicly available gridded predictors were used to analyze the population dynamics in Southern Serbia (South Pomoravlje Region) for the period 1961-2027. Population values from the census administrative units were first downscaled to 200 m grid using a detailed map of populated places and dasymetric interpolation. In the second step, a point pattern representing the whole population (468,500 inhabitants in 2002) was simulated using the R package spatstat. MaxEnt was then used to derive habitat suitability index (HSI) as a function of gridded predictors: distance to roads, elevation, slope, topographic wetness index, enhanced vegetation index and land cover classes. HSI and environmental predictors were further used to explain spatial patterns in the population change index (PCI) through regression modeling. The results show that inhabiting preference for year 1961 is mainly a function of topography (TWI, elevation). The HSI for year 2027 shows that large portions of remote areas are becoming less preferred for inhabiting. The results of cross-validation in MaxEnt show that distribution of population is distinctly controlled by environmental factors (AUC > 0.84). Population decrease is particularly significant in areas >25 km distant from the main road network. The results of regression analysis show that 40% of variability in the PCI values can be explained with these environmental maps, distance to roads and urban areas being the main drivers of migration process. This approach allows precise mapping of demographic patterns that otherwise would not be visible from the census data alone.     

Pixels, blocks of pixels, and polygons: Choosing a spatial unit for thematic accuracy assessment

Pixels, blocks of pixels, and polygons are all potentially viable spatial assessment units for conducting an accuracy assessment. We develop a population-based statistical framework to examine how the spatial unit chosen affects the outcome of an accuracy assessment. The population is conceptualized as a difference map created by overlaying a complete coverage reference classification and the target map being evaluated. The per-class areas of agreement and disagreement derived from this population are summarized by a population error matrix and accuracy parameters (e.g., overall, user's and producer's accuracies). The population and values of the accuracy parameters are strongly affected by the protocols implemented for the response design which include the choice of spatial unit, how within-unit homogeneity is addressed when assigning class labels, and the definition of agreement between the reference and map classification. Several complete coverage populations are used to illustrate how accuracy results are affected by the spatial unit chosen for the assessment and also to evaluate how spatial misregistration of the map and reference locations impacts accuracy results for different spatial units. The sampling design implemented for accuracy assessment does not change the population or values of the accuracy parameters, but the choice of spatial unit will influence decisions regarding use of strata and clusters in the design. A universally best spatial assessment unit does not exist, so it is critical to recognize how the population, values of the accuracy parameters, and sampling design are impacted by the choice of spatial unit.     

Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters

The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, and to support disaster response. Recent studies in disaster informatics have recognized the presence of bias in the representation of social media activity in areas affected by disasters. To explore related factors for such bias, existing studies have used geo-tagged tweets to assess the extent of social media activity in disaster-affected areas to evaluate whether vulnerable populations remain silent on social media. However, less than 1% of all tweets are actually geo-tagged; therefore, attempts to understand the representativeness of geotagged tweets to the general population have shown that certain populations are over- or underrepresented. To address this limitation, this study examined the attention given to locations based on social media content. The study conducted a content-based analysis to filter tweets related to 84 super-neighborhoods in Houston during Hurricane Harvey and 57 cities in North Carolina during Hurricane Florence. By examining the relationships among sociodemographic factors, the number of damage claims, and the volume of tweets, the results showed that social media attention concentrates in populous areas, independent of education, language, unemployment, and median income. The relationship between population and social media attention is characterized by a sub-linear power law, indicating a large variation among the sparsely populated areas. Using a machine-learning model to label the topics of the tweets, the results showed that social media users pay more attention to rescue- and donation-related information; nevertheless, the topic variation is consistent across areas with different levels of attention. These findings contribute to a better understanding of the spatial concentration of social media attention regarding posting and spreading situational information in disasters. The findings could inform emergency managers and public officials to effectively use social media data for equitable resource allocation and action prioritization.     

A spatially explicit agent-based modeling approach for the spread of Cutaneous Leishmaniasis disease in central Iran,Isfahan

Cutaneous Leishmaniasis (CL) is an endemic vector-borne disease in the Middle East and a worldwide public health problem. The spread of CL is highly associated with the socio-ecological interactions of vectors, hosts and the environment. The heterogeneity of these interactions has hindered CL modeling for healthcare preventive measures in endemic areas. In this study, an agent-based model (ABM) is developed to simulate the dynamics of CL spread based on a Geographic Automata System (GAS). A Susceptible-Exposed-Infected-Recovered (SEIR) approach together with Bayesian modeling has been applied in the ABM to explore the spread of CL. The model is then adapted locally for Isfahan Province, an endemic area in central Iran. The results from the model indicate that desertification areas are the main origin of CL, and riverside population centers have the potential to host more sand fly exposures and should receive more preventive measures from healthcare authorities. The results also show that healthcare service accessibility prevented exposures from becoming infected and areas with new inhabitants experienced more infections from same amount of sand fly exposures.     

Population estimation from Landsat imagery

The paper introduces a method of population estimation using the Landsat MSS data. The radiance in the four spectral bands, detected by the multi spectral scanner (MSS) depends upon the ground covering materials, albeit the land use of the area. A mathematical model is set up to express the relation between the reflected electromagnetic energy of sample areas and their population distribution. Landsat 1 and Landsat 3 data of the Kanto area (including Tokyo Metropolitan), acquired in 1972 and 1979, are used along with ground-based census data of 1970 and 1975 to monitor the population distribution and its temporal changes. The method provided a reliable assessment of the population density in residential zones, however land-use classification using MSS imagery previous to the modeling is expected to improve the results.     

人群疏散虚拟现实模拟系统——Guarder

人群疏散的虚拟现实模拟就是利用虚拟现实技术,在计算机生成空间中建立公共设施和人群的三维模型,设定各种可能发生的安全危机和相应的疏散预案,模拟并三维地展示人群疏散场景;通过对模拟结果进行统计分析,可以验证人群疏散应急预案的合理性和有效性.介绍了人群疏散模拟虚拟现实系统Guarder设计的核心思想,提出了技术框架,详细阐述了其中的复杂环境语义表示、群体运动仿真等关键技术,并给出了应用实例,最后列举了几个前沿研究问题.     

基于小生境算法和聚类分析的快速收敛遗传算法

摘要：针对遗传算法中存在的早熟收敛和后期收敛速度慢的问题,在讨论种群多样性表示方法和早熟原因的基础上,提出了一种基于小生境技术和聚类分析的遗传算法快速收敛算法．利用小生境技术保持种群的多样性,有效防止早熟收敛．当种群进化到一定程度后,进行聚类分析,从而获得分布在各个极值点附近的聚类区域．在各个聚类中心处,利用局部搜索算法获得极值点;其余个体按照小生境技术在聚类区域外进一步搜索．仿真结果表明,这种算法能够有效地防止早熟收敛,可以极大提高遗传算法的搜索效率,有利于并行实现,并在一定程度上有助于骗问题的解决．     

A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery

“Urban Sprawl” is a growing concern of citizens, environmental organizations, and governments. Negative impacts often attributed to urban sprawl are traffic congestion, loss of open space, and increased pollutant runoff into natural waterways. Definitions of “Urban Sprawl” range from local patterns of land use and development to aggregate measures of per capita land consumption for given contiguous urban areas (UA). This research creates a measure of per capita land use consumption as an aggregate index for the spatially contiguous urban areas of the conterminous United States with population of 50,000 or greater. Nighttime satellite imagery obtained by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) is used as a proxy measure of urban extent. The corresponding population of these urban areas is derived from a grid of the block group level data from the 1990 U.S. Census. These numbers are used to develop a regression equation between Ln(Urban Area) and Ln(Urban Population). The ‘scale-adjustment’ mentioned in the title characterizes the “Urban Sprawl” of each of the urban areas by how far above or below they are on the “Sprawl Line” determined by this regression. This “Sprawl Line” allows for a more fair comparison of “Urban Sprawl” between larger and smaller metropolitan areas because a simple measure of per capita land consumption or population density does not account for the natural increase in aggregate population density that occurs as cities grow in population. Cities that have more “Urban Sprawl” by this measure tended to be inland and Midwestern cities such as Minneapolis-St. Paul, Atlanta, Dallas-Ft. Worth, St. Louis, and Kansas City. Surprisingly, west coast cities including Los Angeles had some of the lowest levels of “Urban Sprawl” by this measure. There were many low light levels seen in the nighttime imagery around these major urban areas that were not included in either of the two definitions of urban extent used in this study. These areas may represent a growing commuter-shed of urban workers who do not live in the urban core but nonetheless contribute to many of the impacts typically attributed to “Urban Sprawl”. “Urban Sprawl” is difficult to define precisely partly because public perception of sprawl is likely derived from local land use planning decisions, spatio-demographic change in growing urban areas, and changing values and social mores resulting from differential rates of international migration to the urban areas of the United States. Nonetheless, the aggregate measures derived here are somewhat different than similar previously used measures in that they are ‘scale-adjusted’; also, the spatial patterns of “Urban Sprawl” shown here shed some insight and raise interesting questions about how the dynamics of “Urban Sprawl” are changing.     

Ant search based control optimisation strategy for a class of chaotic system

In this paper the authors propose a logistic mapping using chaotic model to describe the time-variable pest population. Two kinds of fuzzy rule embedded control strategies are investigated, three segment control and five segment control. They are designed to reduce the pest population. The simulation results show that the objective function is non-convex and anomalous along the control parameters. To find the optimal parameter combinations we develop an ant search approach. By imitating the food hunting and nest moving behaviours of Pachycondyla apicalis ants, this method can adaptively and effectively explore solution areas and arrive at the optimal solution. When we compared the performance curves with the one without control strategy, the method is better and can be used for a wide range of pest control problems in real life.     

Population spatialization in China based on night-time imagery and land use data

Population is a key indicator of socioeconomic development, urban planning and environmental protection, particularly for developing countries like China. But, census data for any given area are neither always available nor adequately reflect the internal differences of population. The authors tried to overcome this problem by spatializing the population across China through utilizing integer night-time imagery (Defense Meteorological Satellite Program/Operational Linescan System, DMSP/OLS) and land-use data. In creating the population linear regression model, night-time light intensity and lit areas, under different types of land use, were employed as predictor variables, and census data as dependent variables. To improve model performance, eight zones were created using night-time imagery clustering and shortest path algorithm. The population model is observed to have a coefficient of determination (R ²) ranging from 0.80 to 0.95 in the research area, which remained the same in different years. A comparison of the results of this study with those of other researchers shows that the spatialized population density map, prepared on the basis of night-time imagery, reflects the population distribution character more explicitly and in greater detail.     

IMMIGRANTS-ENHANCED MULTI-POPULATION GENETIC ALGORITHMS FOR DYNAMIC SHORTEST PATH ROUTING PROBLEMS IN MOBILE AD HOC NETWORKS

One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time as a result of energy conservation or node mobility. Therefore, the shortest path (SP) routing problem turns out to be a dynamic optimization problem in mobile wireless networks. In this article, we propose to use multi-population genetic algorithms (GAs) with an immigrants scheme to solve the dynamic SP routing problem in mobile ad hoc networks, which are the representative of new generation wireless networks. Two types of multi-population GAs are investigated. One is the forking GA in which a parent population continuously searches for a new optimum and a number of child populations try to exploit previously detected promising areas. The other is the shifting-balance GA in which a core population is used to exploit the best solution found and a number of colony populations are responsible for exploring different areas in the solution space. Both multi-population GAs are enhanced by an immigrants scheme to handle the dynamic environments. In the construction of the dynamic network environments, two models are proposed and investigated. One is called the general dynamics model, in which the topologies are changed because the nodes are scheduled to sleep or wake up. The other is called the worst dynamics model, in which the topologies are altered because some links on the current best shortest path are removed. Extensive experiments are conducted based on these two models. The experimental results show that the proposed multi-population GAs with immigrants enhancement can quickly adapt to the environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.     

Impact of urban planning on household's residential decisions: An agent-based simulation model for Vienna

Interest in assessing the sustainability of socio-ecological systems of urban areas has increased notably, with additional attention generated due to the fact that half the world's population now lives in cities. Urban areas face both a changing urban population size and increasing sustainability issues in terms of providing good socioeconomic and environmental living conditions. Urban planning has to deal with both challenges. Households play a major role by being affected by urban planning decisions on the one hand and by being responsible – among many other factors – for the environmental performance of a city (e.g. energy use). We here present an agent-based decision model referring to the city of Vienna, the capital of Austria, with a population of about 1.7 million (2.3 million within the metropolitan area, the latter being more than 25% of Austria's total population). Since the early 1990s, after decades of negative population growth, Vienna has been experiencing a steady increase in population, mainly driven by immigration. The aim of the agent-based decision model is to simulate new residential patterns of different household types based on demographic development and migration scenarios. Model results were used to assess spatial patterns of energy use caused by different household types in the four scenarios (1) conventional urban planning, (2) sustainable urban planning, (3) expensive centre and (4) no green area preference. Outcomes show that changes in preferences of households relating to the presence of nearby green areas have the most important impact on the distribution of households across the small-scaled city area. Additionally, the results demonstrate the importance of the distribution of different household types regarding spatial patterns of energy use.     

具有重组学习和混合变异的动态多种群粒子群优化算法

为解决粒子群优化算法中种群多样性与收敛性间的矛盾,提出一种具有重组学习和混合变异的动态多种群粒子群优化算法.该算法动态划分多种群并融入重构粒子作为引导因子,在增加种群多样性的同时保留优秀粒子的空间信息;在算法执行阶段对最优个体施加混合变异,基于时变概率实施反向学习策略或者邻域扰动操作,帮助粒子快速跳出局部困境,加强对附近区域内的精细搜索.基于14个多类型标准测试函数,并与其他的改进粒子群算法进行对比,验证了几种改进措施的有效性和叠加影响.为进一步探究概率性混合变异策略的敏感性,对变异方式及参数设置进行仿真实验,结果表明,所采用的极值扰动策略具有显著的优势,合理地控制学习强度可以充分发挥反向学习的作用,并给出影响参数的建议取值范围.实验结果还表明,所提出的算法能够更好地平衡种群的开发与勘探能力,提高求解精度和收敛性能.     

Integration of simulation and optimization for evacuation planning

Evacuation is a time critical process in which the highest priority is to get those people who may be affected by a disaster out of the danger zone as fast as possible. For disaster-prone areas, authorities often distribute evacuation plans well in advance, or encourage the population to prepare themselves for eventual disasters. This paper presents an approach to such planning ahead for evacuation that tightly couples optimization and traffic simulation in order to determine optimal evacuation time and exit from the area for each evacuee. In this paper, we discuss the approach’s properties and illustrate its performance using two case studies of wildfire-prone areas in the state of Victoria, Australia. The results show that our approach can lead to significant improvements when compared to ad-hoc evacuation, but these improvements also strongly depend on population density and road network topology. More generally, our research highlights the significant benefits of tightly coupling optimization and simulation for evacuation modeling.     

Urban growth trend analysis using GIS techniques—a case study of the Bombay metropolitan region

Abstract

Towns and cities in India are facing complex problems regarding the provision and maintenance of services and infrastructure in the face of the rapid growth of pupulation, caused both by natural increase and migration. In order to meet such challenges a planner needs to have fairly accurate and up-to-date information, especially about physical structures and related land parameters. A study of their trends generally helps in the understanding of the emerging growth pattern and in formulating policies to guide or redirect it. Recent advances in the field of remote sensing technology and computer based Geographical Information Systems (GIS) provide very useful tools in undertaking such analysis. The results from a study about growth trends of the urban areas in the Bombay Metropolitan Region using multi-date remote sensing data and ARC/INFO GIS package are described here. The period under consideration is from 1968 to 1989. It should be noted that the major growth in the region is confined to Greater Bombay between 1968 to 1975. After the year 1975, a distinct outward growth along the rail corridors is visible. Growth after 1975 is mainly in the Kalyan, Bhiwandi, New Bombay and Panvel areas and in the area around Manori creek (Charkop area). The growth rate is found to be higher after the year 1975 compared to the prior growth rate. The spatial growth trends are examined in relation to the population and the population density has been computed for different periods. Based upon these densities, the extent of land required for urban development for the year 2001 has been calculated. Suitability of land for urbanisation has been carried out based upon physical characteristics of the land and environmental parameters. The priority areas of urban development to meet the additional requirement in 2001 have been identified on the basis of this suitability analysis. A map on a 1:250000 scale has been prepared to show the areas for urbanisation which will meet the demands for the year 2001. This study demonstrates the potential offered by the integration of the tools of remote sensing and GIS for urban and regional planning.     

Application of a normalized difference impervious index (NDII) to extract urban impervious surface features based on Landsat TM images

The ecological conditions of urban areas have been deteriorating in some aspects due to population growth and increasing expansion, with significant effects on human health. Impervious surface areas are an important indicator of urban ecological environmental change, therefore quickly and accurately estimating impervious surface areas is essential to monitoring the urban dynamics of change and human activities and their effects on urban environmental quality. Currently, few methods that are applied in estimating urban impervious surfaces are capable of providing results quickly and accurately. Accordingly, this study proposes a new index, named the normalized difference impervious index (NDII), based on Landsat TM images, which uses the visible (red, green, and blue) and thermal bands. The index was used to extract the impervious surface areas of Nanjing city, Jiangsu Province, China, and we assume that the average value of five times strict supervised classification is the true value of impervious surfaces. A combination of red and thermal bands extracted the impervious surfaces with a producer’s accuracy of 86.9%, a user’s accuracy of 84.6%, an overall accuracy of 91.4%, and a kappa coefficient of 0.8. The accuracy is 87.7% validated by high-resolution images. This method can rapidly extract urban impervious surface areas with promising accuracy.