Urban phenology: Toward a real-time census of the city using Wi-Fi data

New streams of data are being generated by a range of in-situ instrumentation, mobile sensing, and social media that can be integrated and analyzed to better understand urban activity and mobility patterns. While several studies have focused on understanding flows of people throughout a city, these data can also be used to create a more spatially and temporally granular picture of local population, and to forecast localized population given some exogenous environmental or physical conditions. Effectively modeling population dynamics at high spatial and temporal resolutions would have significant implications for city operations and policy, strategic long-term planning processes, emergency response and management, and public health.This paper develops a real-time census of the city using Wi-Fi data to explore urban phenology as a function of localized population dynamics. Using Wi-Fi probe and connection data accounting for more than 20,000,000 data points for the year 2015 from New York City's Lower Manhattan neighborhood – combined with correlative data from the U.S. Census American Community Survey, the Longitudinal Employer-Household Dynamics survey, and New York City administrative records – we present a model to create real-time population estimates classified by residents, workers, and visitors/tourists in a given neighborhood and localized to a block or geolocation proximate to a Wi-Fi access point. The results indicate that the approach has merit: we estimate intra-day, hourly worker and resident population counts within 5% of survey validation data. Our building-level test case demonstrates similar accuracy, estimating worker population to within 1% of the reported building occupancy.     

Estimating work and home population using lidar-derived building volumes

As urban populations rapidly rise worldwide, it is increasingly necessary to determine the accurate distribution and configuration of the population in order to appropriate resources and services. Census-based methods for obtaining population counts are time consuming, labour intensive, and costly. Researchers have turned to remote sensing to estimate population from aerial and satellite datasets including lidar, which allows measures of building volume to be incorporated into population estimates. However, studies using lidar-derived building volumes have noted inconsistencies between population and building volume estimates in certain areas. In this article, we investigate this issue by incorporating both static and ambient population data into models using the US Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) database. To do this, we first develop a normalized home–work index to classify census blocks as primarily work-oriented, home-oriented, or mixed-use based on the LEHD data. We then employ ordinary least squares and geographically weighted regression (GWR) to explore the relationships between the different population groups (work, home, and mixed) and lidar-derived building volumes. We test these relationships across four diverse cities in Texas: Austin, Dallas, Houston, and San Antonio. Results suggest non-stationarity in the relationship between building volume and population with stronger, positive relationships in home-oriented and mixed-use blocks where the amount of building volume per person may be more consistent compared to work-oriented blocks. GWR models yielded high R² values (0.9), particularly in mixed-use areas, indicating the potential for predictive relationships.     

Demographic data organization and microcomputers

This paper explores the development of a microcomputer-based framework for collecting, organizing and using demographic, economic and land use data in the analysis of cities and regions. There is an increasing amount of U.S. Census and similar data available on floppy and laser disks, and from federal bulletin boards. This data, coupled to the more traditional mainframe sources, represent a valuable resource for the professional. However, data alone are not sufficient. This paper presents a framework for organizing and using such data. Such a framework is built around structured spreadsheet design principles, modular programming concepts, and relational data structures. A topology of typically-used data structures will be presented, along with an overview of a structured urban model system (SUMS).     

基于城镇居民用地再分类的人口数据空间化方法研究——以长江中游4省为例

基于土地利用数据的人口统计数据空间化方法,在处理过程中会出现同一土地利用类型下人口难以细分的情况,从而影响人口空间数据精度。引入夜间灯光信息并提出了一种基于夜间灯光强度对城镇居民地再分类的人口空间化方法,以改善人口空间数据精度。基于DMSP/OLS夜间灯光及土地利用数据,以长江中游4省为研究区进行方法试验。研究结果显示:利用夜间灯光数据对城镇居民地再分类后,各分区模型的调整R2都提高到了0.8以上,人口空间数据总体平均相对误差较重分类前降低了12.32%。说明该方法在提高传统人口数据空间化模型精度的基础上能够细化城镇居民地人口空间分布。     

Later-Life Migrations in Canada in 2001: A Multilevel Approach

Canada is an aging society; with over 13% of the population 65 and over in 2008 and with this population growing at a rate more than double the overall population. Moreover, the aging of the Canada population varies geographically across the nation. Using data drawn from the 2001 Census of Canada Master files (20% sample), the objectives of this analysis are twofold. First, the analysis examines the internal migration behavior of the older population, distinguishing between the native-born and foreign-born. Second, the analysis examines how residential attributes at the census subdivision (CSD) and census metropolitan levels influence the migration decision. This analysis examines the later-life migrations of Canada’s older population at the census subdivision level in 2001.     

基于相似性搜索的水利普查数据融合

针对第一次全国水利普查数据融合存在的问题,提出自适应编辑距离相似性度量,通过调整编辑操作权重及启发式学习权重等措施,对传统的编辑距离进行改进,提高相似性搜索的准确性,并给出基于编辑距离的水利普查数据融合的方法和流程,算法的有效性在第一次全国水利普查数据处理中得到验证。     

Analyzing urban population data using generalized gamma model and wave-spectrum relation: A case study of Kaohsiung

A quantitative understanding of complex urban growth patterns and processes is crucial to sustainable land management and urban development planning in cities. The spatial organization of urban patterns can be treated as fractals and can be characterized with fractal dimension. However, the calculation of fractal dimension of urban form is often constrained by imperfect and incomplete higher temporal resolution land-use data. Because census data are easily acquired, this study aims to provide a systematic investigation of the relationships between population and urban growth by analyzing changes in urban form that are characterized by fractal dimensions. If the population density in cities follows the negative exponential distribution in proximity, we can use the generalized gamma model and wave-spectrum relation to indirectly estimate the fractal dimension of land-use form in cities. Correlogram analysis is performed to consolidate the results from wave-spectrum relation. Information entropy of the city’s population distribution profile along the radial is calculated to measure the degree of spatial dispersion. The schematic framework is applied to the city of Kaohsiung to get significant insight in the dynamics of pattern formation of the urban population. This is critical for further computer-simulated experiments on urban growth and spatial complexity.     

Scaling of urban economic outputs: insights both from urban population size and population mobility

Urban scaling laws assume that the performance of a city largely relies on its urban population size. However, two cities with the same population size may have vastly different economic outputs, which reveals that factors apart from urban size (a measurement of intra-urban interactions) determine their economic outputs. Economic production is essentially the product of social interactions. Urban population size and interurban interactions reflected by population mobility were both considered to evaluate the scaling of urban economic outputs in this paper. We quantified the scaling relationship between urban economic outputs and interurban interactions, and compared it with the paradigm derived from urban size. Results showed that urban economic outputs scale with urban interactions across cities and present the same super-linear scaling regimes but with a greater scaling exponent. A deeper looking showed that interurban interactions definitely bring a more obvious super-linearity than population size but the scaling relationship between urban size and economic outputs is more robust. Urban population size has a greater impact on Gross Domestic Product (GDP), secondary and tertiary industries output products, total retail sales of consumer goods and total wage with 60– 65% relative contribution. For exports, interurban interactions and urban population size are almost equally important. We also proved that interactions between cities are significantly positively correlated with urban extra growth. These findings provide convictive evidence that in addition to population size, interurban interaction is also crucial for exploring the scaling of urban growth. Our results are enlightening to the study of mechanisms and evolutions of urban scaling that interurban interaction besides urban population size should both be a vital consideration to urban economic outputs.     

Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing

Developing low carbon cities is a key goal of 21st century planning, and one that can be supported by a better understanding of the factors that shape travel behaviour, and resulting carbon emissions. Understanding travel based carbon emissions in mega-cities is vital, but city size and often a lack of required data, limits the ability to apply linked land use, transport and tactical transport models to investigate the impact of policy and planning interventions on travel and emissions. Here, we adopt an alternative approach, through the development of a static spatial microsimulation of people’s daily travel behaviour. Using Beijing as a case study, we first derive complete activity-travel records for 1026 residents from an activity diary survey. Then, using the 2000 population census data at the sub-district level, we apply a simulated annealing algorithm to create a synthetic population at fine spatial scale for Beijing and spatially simulate the population’s daily travel, including trip distance and mode choice at the sub-district scale. Finally, we estimate transport CO₂ emission from daily urban travel at the disaggregate level in urban Beijing.     

An integrated classification scheme for mapping estimates and errors of estimation from the American Community Survey

Demographic and socio-economic information provided by the American Community Survey (ACS) have been increasingly relied upon in many planning and decision making contexts due to its timely and current estimates. However, ACS estimates are well known to be subject to larger sampling errors with a much smaller sample size compared with the decennial census data. To support the assessment of the reliability of ACS estimates, the US Census Bureau publishes a margin of error at the 90% confidence level alongside each estimate. While data error or uncertainty in ACS estimates has been widely acknowledged, little has been done to devise methods accounting for such error or uncertainty. This article focuses on addressing ACS data uncertainty issues in choropleth mapping, one of the most widely used methods to visually explore spatial distributions of demographic and socio-economic data. A new classification method is developed to explicitly integrate errors of estimation in the assessment of within-class variation and the associated groupings. The proposed method is applied to mapping the 2009–2013 ACS estimates of median household income at various scales. Results are compared with those generated using existing classification methods to demonstrate the effectiveness of the new classification scheme.     

中国人口普查地理支撑技术研究

介绍了人口普查地理支撑工作,论述了地理信息在人口普查工作中的作用,针对目前人口普查地理信息应用的现状,指出了其在数据收集、管理和应用等方面的不足,进而提出了5种解决方案。这些方案在全国人口普查试点工作中进行了检验,并对它们进行了比较、分析和评价,归纳了各种方案的适用性,供第六次全国人口普查工作参考。     

Landsat urban mapping based on a combined spectral-spatial methodology

Urban mapping using Landsat Thematic Mapper (TM) imagery presents numerous challenges. These include spectral mixing of diverse land cover components within pixels, spectral confusion with other land cover features such as fallow agricultural fields and the fact that urban classes of interest are of the land use and not the land cover category. A new methodology to address these issues is proposed. This approach involves, as a first step, the generation of two independent but rudimentary land cover products, one spectral-based at the pixel level and the other segment-based. These classifications are then merged through a rule-based approach to generate a final product with enhanced land use classes and accuracy. A comprehensive evaluation of derived products of Ottawa, Calgary and cities in southwestern Ontario is presented based on conventional ground reference data as well as inter-classification consistency analyses. Producer accuracies of 78% and 73% have been achieved for urban ‘residential’ and ‘commercial/industrial’ classes, respectively. The capability of Landsat TM to detect low density residential areas is assessed based on dwelling and population data derived from aerial photography and the 2001 Canadian census. For low population densities (i.e. below 3000 persons/km²), density is observed to be monotonically related to the fraction of pixels labeled ‘residential’. At higher densities, the fraction of pixels labeled ‘residential’ remains constant due to Landsat's inability to distinguish between high-rise apartment dwellings and commercial/industrial structures.     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

An estimation of housing vacancy rate using NPP-VIIRS night-time light data and OpenStreetMap data

ABSTRACT

As an informative proxy measure for a range of socio-economic variables, satellite-derived night-time light (NTL) data have been widely used to investigate the diverse anthropogenic activities and reveal urbanization development. Due to the rapid increase of Chinese urbanization rate, from 25.3% in 1987 to 58.5% in 2017, and ‘crazy expansion’ of city space, the sick phenomenon – ‘Ghost Town’ – has been brought out, generally defined as places with high housing vacancy rate (HVR), which will cause the huge waste of the limited land source in China. To investigate the HVR of urban areas in China, this study attempts to establish a hybrid model combining data derived from National Polar-Orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL sensors with OpenStreetMap (OSM) data. By distinguishing non-residential areas and introducing detailed residential building information, we proposed a novel HVR estimation model, thus realizing the estimation of HVR in 31 Chinese provincial cities with different development levels (Tier 1–Tier 3). The results showed the average HVR of Tier 2 cities (0.204) was higher than that of Tier 1 cities (0.189) and Tier 3 cities (0.233). The model was proven more accurate (root mean square error of approximation (RMSE) = 0.022) when compared with previous models. To explore the reasons causing different HVRs in these provincial cities, the relationship between HVR and typical socio-economic factors – gross domestic product (GDP), population, and housing price – was also revealed. Through correlation verification and built of a regression model, HVR was found positively correlated with housing price (0.409), however, negatively correlated with population (?0.829) and GDP (?0.356). The research is an indication of the applicability of using data derived from NPP-VIIRS NTL sensors in reflecting HVR and an exploration to distinguish socio-economic factors influencing HVR in different cities. The model we proposed can potentially provide guidance for urban planners to formulate better land-use plan and rental measures.     

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.     

Indexing with coded deltas—a data compaction technique

The paper describes the coded delta scheme, which is one of the methods used by the Census Research Unit, University of Durham, for compacting the 1971 U.K. census data. It evaluates the merits and limitations of the technique in relation to the characteristics of the data set and other techniques available for compact encoding of numeric and string data.     

Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

Population density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km² with a root mean squared error (RMSE) of 71.56 persons/km², and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km² cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.     

A large-scale study of cultural differences using urban data about eating and drinking preferences

Traditional ways to study urban social behavior, e.g. surveys, are costly and do not scale. Recently, some studies have been showing new ways of obtaining data through location-based social networks (LBSNs), such as Foursquare, which could revolutionize the study of urban social behavior. We use Foursquare check-ins to represent user preferences regarding eating and drinking habits. Considering datasets differing in terms of volume of data and observation window size, our results indicate that spatio-temporal eating and drinking habits of users voluntarily expressed in LBSNs has the potential to explain cultural habits of the users. From this, we propose a methodology to identify cultural boundaries and similarities across populations at different scales, e.g., countries, cities, or neighborhoods. This methodology is extensively evaluated in several aspects. For instance, by proposing some variations of it disregarding some of the considered dimensions, as well as analyzing the results using datasets from different periods and window of observation. The results indicate that our proposed methodology is a promising approach for automatic cultural habits separation, which could enable new urban services.