Spatial Distribution Of Urban Population Research Articles

Causal effect of metro operation on regional resident mobility considering zone-based trip time reliability

The full use of underground space is recognized as a necessary means to improve the accessibility of residents and change the spatial distribution of urban population. The dividends generated by the metro operation will increase as the increases in the number of residents attracted to the zone. However, when the number of residents within the zone exceeds a certain threshold, travel accessibility may be reduced by exceeding the optimal service capacity of the metro, which leads to a decline in the per capita dividend. This paper uses the zone-based trip time reliability (TTR) to represent travel accessibility, and describes the interaction between the zone-based TTR and regional resident mobility. A generalized boosted method based Rubin causal model with longitudinal data was adopted to investigate the impacts of the improvement of zone-based TTR by metro operation on regional resident mobility. Then, an approximate BPR function was established to represent the feedback impact of regional resident mobility on zone-based TTR. Five typical areas considering population, average age, income level and land value are proposed to recognize the resident spatial re-segmentation rule. The results show that the metro operation has a significant impact on the regional resident mobility. And the improvement of zone-based TTR by metro operation is more attractive for resident relocation in Mixed and Stable areas than in Vibrant and Siphonic areas under the similar location distance. This paper intends to indicate the trade-off between zone-based trip time reliability (TTR) and regional resident mobility, and provide a reference for the development and utilization of urban underground space.

Data-enriched interpolation for temporally consistent population compositions

This research evaluates the performance of areal interpolation coupled with dasymetric refinement to estimate different demographic attributes, namely population sub-groups based on race, age structure and urban residence, within consistent census tract boundaries from 1990 to 2010 in Massachusetts. The creation of such consistent estimates facilitates the study of the nuanced micro-scale evolution of different aspects of population, which is impossible using temporally incompatible small-area census geographies from different points in time. Various unexplored ancillary variables, including the Global Human Settlement Layer (GHSL), the National Land-Cover Database (NLCD), parcels, building footprints and the proprietary ZTRAX® dataset are utilized for dasymetric refinement prior to areal interpolation to examine their effectiveness in improving the accuracy of multi-temporal population estimates. Different areal interpolation methods including Areal Weighting (AW), Target Density Weighting (TDW), Expectation Maximization (EM) and its data-extended approach are coupled with different dasymetric refinement scenarios based on these ancillary variables. The resulting consistent small area estimates of white and black subpopulations, people of age 18–65 and urban population show that dasymetrically refined areal interpolation is particularly effective when the analysis spans a longer time period (1990–2010 instead of 2000–2010) and the enumerated population is sufficiently large (e.g., counts of white vs. black). The results also demonstrate that current census-defined urban areas overestimate the spatial distribution of urban population and dasymetrically refined areal interpolation improves estimates of urban population. Refined TDW using building footprints or the ZTRAX® dataset outperforms all other methods. The implementation of areal interpolation enriched by dasymetric refinement represents a promising strategy to create more reliable multi-temporal and consistent estimates of different population subgroups and thus demographic compositions. This methodological foundation has the potential to advance micro-scale modeling of various subpopulations, particularly urban population to inform studies of urbanization and population change over time as well as future population projections.

Estimating changes in urban land and urban population using refined areal interpolation techniques

Abstract. The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Target Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combinations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead, it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.

Modeling the spatial distribution of urban population during the daytime and at night based on land use: A case study in Beijing, China

Urban population during the daytime and at night and their spatial distribution are important bases for planning urban infrastructure, public services and disaster relief. As current population statistics cannot distinguish urban population during the daytime from that at night, existed research in this field are quite limited. This paper tries to advance studies at this aspect by establishing a relationship model for the three components of ‘population, land use and time (daytime or night)’ to explore the temporal and spatial characteristics of different types of population, which is aimed to estimate urban population during the daytime and at night and to analyze their spatial characteristics at grid scale. Furthermore, an empirical case study has been carried out at the Haidian District in Beijing, China to test the model. The results are as follows: (1) The spatial structure of urban population during the daytime is significantly different from that at night. The spatial distribution of urban population during the daytime is more extensive and more agglomerated that that at night. (2) Several types of spatial coupling relationship between population during the daytime and that at night have been identified, such as sandwich mode, symmetry mode, convergence mode and single mode, etc. (3) The spatial distribution of daytime and nighttime population also reflects certain factors during the development of China, such as the distribution of old residential areas, the construction of new industrial districts, and the differences between urban and rural areas, which can provide reference points for studies in this field and other regional research.

Fractal-based exponential distribution of urban density and self-affine fractal forms of cities

Urban population density always follows the exponential distribution and can be described with Clark’s model. Because of this, the spatial distribution of urban population used to be regarded as non-fractal pattern. However, Clark’s model differs from the exponential function in mathematics because that urban population is distributed on the fractal support of landform and land-use form. By using mathematical transform and empirical evidence, we argue that there are self-affine scaling relations and local power laws behind the exponential distribution of urban density. The scale parameter of Clark’s model indicating the characteristic radius of cities is not a real constant, but depends on the urban field we defined. So the exponential model suggests local fractal structure with two kinds of fractal parameters. The parameters can be used to characterize urban space filling, spatial correlation, self-affine properties, and self-organized evolution. The case study of the city of Hangzhou, China, is employed to verify the theoretical inference. Based on the empirical analysis, a three-ring model of cities is presented and a city is conceptually divided into three layers from core to periphery. The scaling region and non-scaling region appear alternately in the city. This model may be helpful for future urban studies and city planning.

Simulation analysis on spatial pattern of urban population in Shenyang City, China in Late 20th century

The spatial distribution of urban population can reflect significantly urban functions and development status. Shenyang, as a typical old industrial city in China, has experienced considerable changes in spatial distribution of population in the process of urban transformation, resulting in the change of urban spatial structure. Based on the subdistrict data of Chinese national population censuses in 1982, 1990 and 2000, this study simulates the evolution pattern of spatial distribution of urban population in Shenyang City. Using statistical method and exploratory spatial data analysis (ESDA), we found that the population distribution, on the whole, has presented a balanced and decentralized trend since the 1980s, which characterizes with Chinese suburbanization. Furthermore, based on the investigation of the pattern of population distribution, it is concluded that the negative exponential model fitted the distribution best, and population concentration in the inner suburb kept increasing gradually, meanwhile, the spatial structure of population distribution has presented a polycentric feature since the 1980s. The parameters of the model show that population in the urban core concentrate significantly all the time. The increase of population in the inner suburb influences the population distribution pattern more and more importantly, but the concentration intensity of population cores in inner suburb is still low.

Study on Simulating Spatial Distribution and Varying Patterns of Urumqi's Population

Abstract In this paper, an approach to simulate the spatial distribution of urban population is proposed using urban land use and population statistical data through the geographical information systems (GIS). Then, the spatial population distribution of Urumqi as a case is simulated by the approach mentioned above and its varying patterns are analyzed by the raster population surface. As a result, producing raster population surface is more accurate and natural than the traditional choropleth map of population density. Concerning the spatial population distribution of Urumqi, the population density declines from south to north and the population distribution mainly presents “T-type”, the population distribution presents multi-centre agglomeration and the population distribution of the districts shows different features. The population density varies significantly with the increase in the distance from central business district (CBD). Finally, it is found in this paper that the development history of dist...

A Spatial Analysis of Urban Population Distribution in Raleigh, North Carolina

A Spatial Analysis of Urban Population Distribution in Raleigh, North Carolina Chittaranjan Pathak Indian Institute of Technology, Kharagpur Raleigh is the easternmost city of the so-called "Piedmont Crescent," a string of small- to medium-size cities along a semi-circular course extending westward from Raleigh through Greensboro, Charlotte, and into South Carolina. These cities, interconnected by railroads and by state, interstate and federal highways, are spreading along the transportation lines—as well as laterally—with a tendency to form a continuous chain of urban development (Fig. 1). Another distinctive feature of the Crescent is the absence of one dominant metropolis. Among the North Carolina cities involved, the largest was Charlotte with a 1960 population of 201,564, followed by Greensboro with 119,574, Winston-Salem with 111,134, and Raleigh with 93,931. Within this Crescent context, Raleigh has experienced a distinct growth pattern. It is the capital of North Carolina and the seat of Wake County. This political-administrative function brought Raleigh economic stability and, originally, a slow but steady growth in population and physical size. The distribution of population in Raleigh was simple and compact prior to this century, with a much smaller area size and a lower population. Even until very recently population density changes in the city have not been marked, and low density has been a Raleigh characteristic. The rapid rate of growth of Raleigh has occurred mostly in the twentieth century, particularly since the 1930's. In the period 1930-1960, Raleigh increased in population by 151 per cent, in area by 376 per cent, and in employment by 134 per cent. As a matter of fact, city growth in the 1950's exceeded that of any previous decade in the city's history. The population of Raleigh increased to 93,931 in 1960 with a city area of 33.646 square miles, which more than doubled the 1940 population and tripled the city area. The post-war population influx swelled the city population and was accompanied by rapid growth in city size and urban functions . Until 1920, the city shape was a perfect square of four square miles in area; since then, the city has grown axially mostly to the north and west to the present size with a wedge-like city shape. (1) While government and professional activities—the two basic urban functions of Raleigh—continued to set the pace for city growth and remained dominant through the present day, several other functions also grew notably. Raleigh became an important financial center in the state. Retail trade increased fivefold in the 1950's, and wholesale business made it the leading distribution center in the eastern part of the state. Although manufacturing had only 9.3 per cent of total employment in 1960, it increased by 74 per ___. RESEARCH TRIANGLE AREA WINSTON-SALEM ; ^. ^~ ? Burlington: DURHAM GREENSBORO HIGH ALEIGH"^. Concord/NORTH CAROLINA PIEDMONT CRESCENT HARLOTTE RALEIGH 40 mi INDEX MAP C/3 Q O W 1 Figure 1. Raleigh's location in North Carolina Piedmont Crescent. Vol. IV, 196443 cent in employment, and even more in the value added by manufacture during the 1950's. These changing and growing urban functions made Raleigh more multi-functional than any other city of North Carolina. (2) This rapid city growth and many other socio-economic factors have complicated the population structure of Raleigh and have altered its distribution . The main purpose of this study is to measure quantitatively the present day spatial distribution of urban population in Raleigh and to measure the extent to which its population structure has been changed due to rapid city growth. PATTERN OF POPULATION DISTRIBUTION. Until 1960 more people lived outside than inside the central city in Wake County. This was also true for Raleigh Township until 1930. (3) By 1940, Raleigh Township had about 50 per cent of the county population. During the decade 1940-1950, an increase in population was noted in Wake County townships having central towns, with maximum increase in Raleigh Township. The central city as well as other towns of Wake County gained in population both numerically and in percentage share of county total at the expense of agricultural townships with no important towns...