Enhancing areal interpolation frameworks through dasymetric refinement to create consistent population estimates across censuses

To assess micro-scale population dynamics effectively, demographic variables should be available over temporally consistent small area units. However, fine-resolution census boundaries often change between survey years. This research advances areal interpolation methods with dasymetric refinement to create accurate consistent population estimates in 1990 and 2000 (source zones) within tract boundaries of the 2010 census (target zones) for five demographically distinct counties in the US. Three levels of dasymetric refinement of source and target zones are evaluated. First, residential parcels are used as a binary ancillary variable prior to regular areal interpolation methods. Second, Expectation Maximization (EM) and its data-extended version leverage housing types of residential parcels as a related ancillary variable. Finally, a third refinement strategy to mitigate the overestimation effect of large residential parcels in rural areas uses road buffers and developed land cover classes. Results suggest the effectiveness of all three levels of dasymetric refinement in reducing estimation errors. They provide a first insight into the potential accuracy improvement achievable in varying geographic and demographic settings but also through the combination of different refinement strategies in parts of a study area. Such improved consistent population estimates are the basis for advanced spatio-temporal demographic research.     

Interpolating U.S. Decennial Census Tract Data from as Early as 1970 to 2010: A Longitudinal Tract Database

Differences in the reporting units of data from diverse sources and changes in units over time are common obstacles to analysis of areal data. We compare common approaches to this problem in the context of changes over time in the boundaries of U.S. census tracts. In every decennial census, many tracts are split, consolidated, or changed in other ways from the previous boundaries to reflect population growth or decline. We examine two interpolation methods to create a bridge between years, one that relies only on areal weighting and another that also introduces population weights. Results demonstrate that these approaches produce substantially different estimates for variables that involve population counts, but they have a high degree of convergence for variables defined as rates or averages. Finally, the article describes the Longitudinal Tract Database (LTDB), through which we are making available public-use tools to implement these methods to create estimates within 2010 tract boundaries for any tract-level data (from the census or other sources) that are available for prior years as early as 1970.     

Dasymetric Spatiotemporal Interpolation

This research applies the principles of dasymetric mapping to spatiotemporal interpolation by extending the spatial concepts of zone and area to their temporal analogs of interval and duration, respectively. An example application of dasymetric spatiotemporal interpolation using crime event data is presented. Results indicate that dasymetric spatiotemporal interpolation significantly improves the accuracy of estimates over areal or duration weighting. In addition, even when dasymetric interpolation in either the spatial or temporal dimension is relatively weak, combining dasymetric estimation in both space and time dimensions simultaneously has the potential to amplify the accuracy of the overall dasymetric estimation.     

Modeling Sensitivity to Accuracy in Classified Imagery: A Study of Areal Interpolation by Dasymetric Mapping*

Areal interpolation is the process by which data collected from one set of zonal units can be estimated for another zonal division of the same space that shares few or no boundaries with the first. In previous research, we outlined the use of dasymetric mapping for areal interpolation and showed it to be the most accurate method tested. There we used control information derived from classified satellite imagery to parameterize the dasymetric method, but because such data are rife with errors, here we extend the work to examine the sensitivity of the population estimates to error in the classified imagery. Results show the population estimates by dasymetric mapping to be largely insensitive to the errors of classification in the Landsat image when compared with the other methods tested. The dasymetric method deteriorates to the accuracy of the next worst estimate only when 40% error occurs in the classified image, a level of error that may easily be bettered within most remote sensing projects.     

Error‐sensitive historical GIS: Identifying areal interpolation errors in time‐series data

Historical GIS has the potential to re‐invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long‐run time‐series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.     

High resolution dasymetric model of U.S demographics with application to spatial distribution of racial diversity

Population and demographic data at high spatial resolution is a valuable resource for supporting planning and management decisions as well as an important input to socio-economic academic studies. Dasymetric modeling has been a standard technique to disaggregate census-aggregated units into raster-based data of higher spatial resolution. Although utility of dasymetric mapping has been demonstrated on local and regional scales, few high resolution large-scale models exist due to their high computational cost. In particular, no publicly available high resolution dasymetric model of population distribution over the entire United States is presently available. In this paper we introduce a 3″ (∼90 m) resolution dasymetric model of demographics over the entire conterminous United States. The major innovation is to disaggregate already existing 30″ (∼1 km) and 7.5″ (∼250 m) SEDAC (Socioeconomic Data and Applications Center) Census 2000 grids instead of the original census block-level data. National Land Cover Dataset (NLCD) 2001 is used as ancillary information. This allows for rapid development of a U.S.-wide model for distribution of population and sixteen other demographic variables. The new model is demonstrated to markedly improve spatial accuracy of SEDAC model. To underscore importance of high spatial resolution demographic information other than total population count we demonstrate how maps of several population characteristics can be fused into a “product” map that illustrates complex social issues. Specifically, we introduce a “diversity” categorical map that informs (at nominal 3″ resolution) about spatial distribution of racial diversity, dominant race, and population density simultaneously. Diversity map is compared to a similar map based on census tracts. High resolution raster map allows study of race-diversity phenomenon on smaller scale, and, outside of major metropolitan areas, revels existence of patterns that cannot be deduced from a tract-based map. The new high resolution population and diversity maps can be explored online using our GeoWeb application DataEye available at http://sil.uc.edu/. Both datasets can be also downloaded from the same website.     

城市空间增长与人口密度变化之间的关联关系

探究城市空间增长与人口密度变化之间的关联关系是城市可持续发展研究的基础,对制定有效的土地政策、促进城市紧凑发展具有重要意义。选取中国和欧洲23个人口100万以上城市作为研究样本,采用1990年、2000年和2014年3期土地利用数据和人口数据,计算各时段样本城市空间增长速率,建立城市紧凑度指标,并将城市空间增长方式划分为紧凑型、保持型和蔓延型,进一步探讨了样本城市的空间增长特征、人口密度变化特征及两者之间的关系。主要结论为：① 人口密度降低这一现象普遍存在于中国和欧洲城市,中国城市人口密度相对较高且降低速率较快,欧洲城市人口密度相对较低且降低速率较慢;1990—2014年中国城市半径的增长速率明显快于欧洲城市半径的增长速率。城市空间增长速率与人口密度变化速率呈现强负相关关系（Pearson相关系数为-0.693）;② 1990—2000年中欧城市主要以紧凑型方式增长。2000—2014年中国城市主要以蔓延型方式增长,且人口密度随时间下降的速率加快;欧洲城市主要以紧凑型和保持型方式增长,人口密度降低速率减小;③ 紧凑型的空间增长方式并不一定会带来人口密度的升高,但会减缓人口密度下降的速率。中欧城市对比分析表明,城市维持紧凑型空间增长方式,则城市人口密度随时间下降速率减慢;而空间增长方式由紧凑型转为蔓延型,将加剧城市人口密度随时间下降。     

LAND‐USE DYNAMICS BEYOND THE AMERICAN URBAN FRINGE*

ABSTRACT. A deficiency common to both the historical debates over loss of agricultural land and the current discussions of urbanization and sprawl is a limited understanding of land‐use dynamics beyond the urban fringe. Data aggregated at the county level poorly capture the fine‐grained pattern of land‐use change beyond the dynamic urban‐rural interface. Furthermore, current urban‐based definitions are poorly suited to delineate these areas, and low‐density, exurban land use is difficult to measure using existing land‐cover databases. Urbanization and the conversion of once‐agricultural or other natural resource lands to other uses has traditionally been tracked using urban areas, as delimited in the U.S. census. Urban densities are typically defined as areas with more than 1,000 people per square mile, or 1.6 people per acre (U.S. Census Bureau 2000). Assuming an average of 2.5 people per housing unit, this translates to roughly 0.7 units per acre, or approximately 1 unit per 1.6 acres. The analytical units used in the census, however, both overbound and underbound areas with urban densities. About one‐third of urban areas in 1990 comprised lower‐than‐urban housing density, thanks to overbounding. But, then, one‐third of locations that had urban‐level housing densities failed to be included in urban areas as a result of underbounding, which, if counted, would have constituted another 18 million acres of urban area. An increase over time of the average number of acres required per housing unit in exurban and higher‐density locations occurred in roughly one‐third of U.S. counties from 1960 to 1990 and persisted from 1990 to 2000. In 2000 roughly 38 million acres were settled at urban densities, and nearly ten times that much land was settled at rates from low, exurban density (as low as one house per 40 acres) to higher rates (up to one per 10 acres). This represents a continuing encroachment on land previously given over to other uses—habitat or agriculture. Practitioners of natural resource management need to recognize the ubiquity of exurban development and better incorporate the fine‐scale patterns of land use beyond the urban fringe.     

The Fate of Inner Suburbs: Evidence From Metropolitan Baltimore

This paper analyzes the transformation of Baltimore's inner suburbs from 1980 to 2000. After developing a geographic definition of inner suburbs, we then spatially analyze them using census place-level data. The analysis shows evidence of socioeconomic decline in Baltimore's inner suburbs, but the extent of this decline varies among these suburbs. Since 1980, many declining inner suburbs had difficulty attracting new residents, White flight was the prevailing trend, and the housing stock was outdated relative to the outer suburbs. The analysis suggests three major influences on decline among the inner suburbs of Baltimore: labor market restructuring, the nature of the local housing market, and income and racial segregation. This paper concludes with a classification of Baltimore's inner suburbs based on our understanding of the processes of suburban decline in the region.     

Spatial Segregation of the Poor in Southern California: A Multidimensional Analysis

Poverty segregation increased in Southern California during the 1990s. A comparison of four indices reflecting various dimensions of segregation (e.g., evenness, concentration, isolation, and clustering) suggests that poor people are increasingly concentrated and isolated. However, cities within the region experienced different levels and types of segregation, with significant changes over time. Regression analysis using 1990 and 2000 census data highlights the role of demographic, economic, and fiscal variables in explaining the changing landscape of poverty. Whereas historically poor and older cities have seen a rise in poverty that translated into an increase in concentration and isolation, newer suburban communities have evolved differently. Cities where poverty has been high and increasing often maintained a high level of clustering and unevenness, but experienced higher levels of concentration and clustering in 2000. In contrast, wealthier cities typically faced a rise in unevenness and clustering as small pockets of poverty population emerged in the midst of high-income neighborhoods.     

Cropping patterns in the Canadian Prairies: thirty years of change

Changes in cropping patterns in the Canadian Prairies are examined from the early 1960s to the mid-1990s using census data. Patterns of change within the region are mapped by census division using averaged proportions of land in crops occupied by the main crops for three pairs of census years. Spring wheat and oat have undergone the most significant relative declines. Canola increased dramatically from being the sixth-ranked crop by area in the early 1960s to the third-ranked crop by area by the 1990s. The main change in the Brown soil zone has been a large decline in spring wheat and a compensatory gain in durum wheat. Increases in special crops, especially pulse crops, canola and durum wheat have offset a substantial decline in spring wheat in the Dark Brown soil zone. Barley, tame hay and especially canola have increased at the expense of spring wheat, oat and flaxseed in the Black and Gray soil zones. Prices, transportation costs, changing export markets, crop breeding and local processing all have contributed to these changes.     

Incorporating GIS Building Data and Census Housing Statistics for Sub-Block-Level Population Estimation

This article presents a deterministic model for sub-block-level population estimation based on the total building volumes derived from geographic information system (GIS) building data and three census block-level housing statistics. To assess the model, we generated artificial blocks by aggregating census block areas and calculating the respective housing statistics. We then applied the model to estimate populations for sub-artificial-block areas and assessed the estimates with census populations of the areas. Our analyses indicate that the average percent error of population estimation for sub-artificial-block areas is comparable to those for sub-census-block areas of the same size relative to associated blocks. The smaller the sub-block-level areas, the higher the population estimation errors. For example, the average percent error for residential areas is approximately 0.11 percent for 100 percent block areas and 35 percent for 5 percent block areas.     

东北三省人口分布空间格局演化及其驱动因素研究

东北地区作为全国重要老工业基地,在面临新一轮振兴机遇的形势下,人口分布的集疏变化及其驱动因素亟待细致梳理。根据中国第四、第五、第六次分县人口普查资料,运用洛伦兹曲线、人口密度相对变化率和空间自相关分析方法对东北地区1990~2010年、176个县域的人口分布空间格局演化进行深入分析,在此基础上,选取10个自然和社会经济指标,使用偏最小二乘法（PLS）对东北地区人口分布空间格局演化的影响因素进行定量分析,研究发现：①从总体分布格局变化特征来看,20 a来人口密度始终是黑龙江省<吉林省<辽宁省,且以省会城市为主的多中心“T”型人口分布空间格局越来越明显;②1990~2010年,东北地区人口密度经历了从相对分散到相对集中的变化过程,且人口密度高的地区所占土地面积小,人口密度低的地区占地面积大。人口空间分布呈现显著的正的空间自相关特征,即人口密度存在高高、低低集聚现象;③从人口密度变化类型来看,1990~2010年,共80个县域属于快速增加和缓慢增加类型,快速降低和缓慢降低类型的县域共96个,其中辽宁省县域人口密度变化“两极化”现象较为明显;④自然因素与社会经济因素同时作用于人口密度变化,但社会经济发展水平是人口密度变化的主要影响因素。     

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.     

Violence Change and Cohort Trajectories: Baltimore Neighborhoods, 1990-20001

Whereas the relationship between poverty and violence is incontrovertible in the American city, demographic relationships at the neighborhood level are relatively poorly understood. Patterns of violence and population change are examined at the census tract level in Baltimore, Maryland across the decade from 1990 to 2000. Aggravated assault and homicide are combined and used as a composite indicator of serious violence. Population data are examined for selected tracts representative of anomalous outliers experiencing population decline but increased violence. The data are broken down into five-year cohorts represented graphically at their beginning (1990) and end (2000) points. The analysis indicates that some neighborhoods have experienced collapse of youthful cohorts. Normally, a decline in youthful population would predict a reduction in crime, but this analysis suggests that at the neighborhood scale criminogenic processes may affect neighborhoods somewhat independently of their demographic attributes. Most notably, population decline at the neighborhood level may be accompanied by crime increase, contrary to macrolevel theoretical expectation.     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

1990—2015年中国县市尺度人口收缩的演变特征及影响因素

近年来,区域人口收缩问题在全球范围内引起了广泛关注,并且在中国也日益明显。利用1990年、2000年和2010年人口普查数据以及2015年1%人口抽样调查数据,本文在县市尺度上分析了1990—2000年、2000—2010年和2010—2015年三个时期人口收缩区的空间特征及演化趋势,并定量探讨了其形成与演变的影响因素,主要发现如下：① 人口收缩现象在县市尺度非常明显,三个时期人口收缩区占比均超过20%;② 2000—2010年人口收缩区以大面积扩张为主要特征,东北地区、川黔渝地区、长江中游地区最为明显,而东部地区则主要集中在江苏北部和福建西部等局部范围;③ 2010—2015年人口收缩转缓慢增长成为主要特征,仅东北地区、河南等人口收缩区仍有所扩张;④ 人均GDP和非农就业占比等经济发展水平因素对人口收缩区的形成影响显著,而经济发展速度因素则与人口收缩的演变密切相关;⑤ 除经济因素外,人口自然增长因素对人口收缩区形成及演变的影响均非常显著,且其作用呈增强趋势。     

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.     

Mexican and Central American immigrant communities in the boom and bust housing markets of the 2000s

Classic immigrant enclaves, largely voluntary and temporary, have historically served as a strong platform for integration in United States metropolitan areas. However, trends in the early 2000s, including new destinations and skyrocketing housing costs, may have reshaped the landscape, particularly for numerically dominant immigrant groups with very low socioeconomic status. In this article, I use data for 56 metropolitan areas and 31,563 census tracts from the 2000 Census and the 2005–2009 American Community Survey to examine the relationship between the change in concentration of Mexican and Central American immigrants and the characteristics of neighborhoods in 2000. The analyses suggest that, across metropolitan areas, these immigrant communities consolidated in neighborhoods with low home values, adequate but overcrowded housing, relatively small shares of their own immigrant group, and large shares of other Hispanics. Demographic dynamics may have weakened immigrant support networks, leaving Mexican and Central American immigrants vulnerable, particularly in a subset of metropolitan areas where this population grew most substantially outside the central cities.     

Assessing the impact of Celaque National Park on forest fragmentation in western Honduras

The effectiveness of parks as management regimes is much in debate. This study examines the effect of establishment of the Celaque National Park, Honduras, in 1987, on limiting deforestation through a comparison with the surrounding landscape using remote sensing, GIS and landscape pattern analysis. Pressure on the park region is found to relate spatially to the locations of towns and roads, with increasing deforestation in the landscape surrounding the park. In contrast, the park has been largely successful in maintaining forest cover. Although the extent of change within the park is not pronounced, the pattern of change is. Expansion of agriculture and coffee production have led to increasing pressure on the park boundaries, with as much as 25% of the landscape surrounding the park experiencing land cover change between 1996 and 2000. This has significant implications for the future of the park.