Population and employment densities: structure and change

Identification and classification of urban employment centers based on big data: A case study of Beijing

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.

Urban noise exposure assessment based on principal component analysis of points of interest

Accurate qualitative and quantitative information on the characteristics of traffic noise exposure in densely populated urban areas is an important prerequisite for reasonable traffic noise control. The primary objective of this study is the development and application of a traffic noise exposure evaluation method based on points of interest (POIs). First, an automatic query arithmetic is used to acquire geospatial information, POIs data, building and network information from the webmap. Second, the attribute matrix of preprocessed POIs for the population is constructed. And the population distribution is obtained by principal component analysis (PCA) of POIs and Gaussian decomposition of demographic data. Then, the modified traffic noise line-source model is applied to calculate the noise distribution considering attenuation among buildings based on measured traffic flow parameters. Finally, with the help of the proposed noise evaluation indicators, and considering the noise function requirements (NFRs, which can be divided into four classes according to different area land-use types), traffic noise evaluation is realized. The proposed method is applied to a typical region with four NFR classes. It is concluded that the characteristics of traffic noise exposure are affected by traffic conditions, buildings, NFR classes and population distribution. And the crowds exposed to noise present aggregation effects, which are usually centered around specific buildings. In addition, POI types which people actives related suffer more serious noise exposure, and exposure is overestimated at low requirement regions without considering crowd distribution of the setting scenario.

Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance

Numerous studies have suggested a positive correlation between spatial and population densities. However, few have systematically conducted quantitative analysis and deciphered the detailed correlation in block scale. Here, we construct a population–space correlation algorithm to quantify and compare the correlation between mobile phone signalling data and vector spatial data and identify blocks with uneven population density. We analyse the influences of various urban spatial characteristics on population density and the distribution characteristics of the identified city blocks. Changzhou City, China, was selected as the study case. The results indicate that (1) population density distribution is unbalanced only when spatial density exceeds a critical value, reflecting the level and sphere of influence of blocks with varying spatial densities; (2) low population density distribution is concentrated in the zonal space, along the boundary between primary and secondary urban centres; (3) spatial characteristics affecting population density distribution vary with the type of block, and the green landscape’s attractiveness is reduced. Our study provides a novel perspective on quantifying the link between urban form and population distribution. It can help decision-makers and planners in accurately recommending urban intervention in population density distribution by adjusting the spatial morphology and promoting rational use of urban public resources.

City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China

City classification can provide important data and technical support for city planning and government decision-making. Traditional city classification mainly relies on the accumulation and analysis of census data, which requires a large time period and relies heavily on historical and statistical data. This paper mainly utilizes Luojia I Night-Time Light (NTL) images to analyze the rank classification of cities in Henan Province, China. Intensity values can be expressed as the mathematical surface of continuous human activities, and the basic characteristics of urban structures are determined by analogy with the topography of the earth. A connectivity analysis method for NTL images is proposed to analyze the connected regions of images at different intensity levels. By constructing a tree structure, different cities can be analyzed “crosswise” and “lengthwise” to generate a series of parametric information from connected regions of NTL images. Based on these parameters, 18 cities in Henan Province were classified and analyzed. The results show that these attribute information can be well used for city center detection and grade classification, and can meet the requirements of application analysis.

Towards Sustainable Urban Spatial Structure: Does Decentralization Reduce Commuting Times?

This paper contributes to the existing debate on the co-location hypothesis, by devising a proximity measure and controlling for a set of other urban form measures. Utilizing the LEHD (Longitudinal Employer–Household Dynamics) Origin-Destination Employment Statistics (LODES) data that provide the number of jobs by a finer geography, this paper measured the degree of centralization, proximity, and job–housing mismatch. Multiple regression analysis revealed that the job–worker proximity leads to a shorter commuting time. In addition, the results focusing on suburban areas revealed that the impact of the job–worker imbalance and the impact of job–worker mismatch on the commuting time are greater in the suburb in comparison with the city center.

Firm Location in a Polycentric City: The Effects of Taxes and Agglomeration Economies on Location Decisions

The authors explore the determinants of firm location in a polycentric city with the aid of data for the Houston region. Firm location is modeled in a discrete-choice framework, with eight employment centers and outlying areas used as possible choices. Agglomerative and dispersive forces are explicitly treated, as are taxes and other characteristics that vary over space. The findings suggest that property taxes have large deterrent effects on firm locations for the four industrial groups analyzed here: oil and gas; manufacturing; finance, insurance and real estate (FIRE); and services. When agglomeration economies are present, they are weaker than the tax effects and are positive for only the FIRE and services industrial groups.

Explaining Intraurban Variations of Commuting by Job Proximity and Workers' Characteristics

Based on the Census for Transportation Planning Package (CTPP) 1990, in this paper I will attempt to explain intraurban variations of commuting time and distance in Columbus, OH. Whereas commuting times are directly available from the CTPP, commuting distances are recovered by GIS network techniques as the shortest-time distances through the urban road network. Explanations of commuting are first attempted by measures of job proximity such as distances from employment centers and a gravity-based index accounting for job competition among workers. The influence of employment centers on commuting sheds new light on polycentric urban models. The variation of commuting distance is well explained by the gravity-based job proximity index. That is to say, how far people commute is explainable to a large extent by urban land use. However, how much time people spend on commuting is complicated by their socioeconomic status, which affects their choice of commuting modes. Finally the research demonstrates the promise of explaining commuting times by workers' characteristics such as percentage of black workers, female labor participation ratio, percentage of multiple-worker households, home-ownership status, educational attainment, and wage rate. In the analyses, spatially lagged dependent variables are introduced to control for the effect of spatial autocorrelation.

Kernel Densities and Mixed Functionality in a Multicentred Urban Region

Interurban-level focus during the last decade has shifted from the compact city towards a polycentric urban framework. The ability to define consistent urban structures and also link them with sustainability goals has been hindered by inconsistent evaluation methods for density and mixed functionality in a polycentric framework. The aim of this research is to test and combine various methods from these perspectives in order to define more reliable and consistent descriptions of urban structures. The methods used are spatial-density modelling using kernel convolution, a polycentric density estimation, and methods depicting mixed functionality and the association between density and mixed functionality. The empirical findings relate to planning goals at both national and international level. The study region is the municipality of Strängnäs, within the Stockholm City Region since 1997. Results from the analysis reveal urban development towards further segregated land use and sprawl, as well as a decreasing link with a polycentric urban scheme. The methods developed for depicting urban form could be useful tools in the planning process and may reinforce the possibility for analysing links between urban form and sustainability aspects. This improved knowledge in turn could contribute towards formulating future planning principles.