Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

Animation of Reliability in Computer-generated Dot Maps and Elevation Models

Visualization of reliability in spatial data has been the subject of considerable recent research activity. Animation has been suggested as one method to achieve this, and its application to various measures associated with class-area maps (classified satellite images and soil maps) has been discussed elsewhere. Animation is achieved by randomly selecting a location and then assigning it to a different map unit (cover or soil type) according to the information on the reliability associated with the original map or map units. In this article the same basic method is extended to mapping locational reliability in dot maps and surface error in a digital elevation model (DEM). In the former case, the dots, which are randomly located in the first place, are randomly relocated so that any meaningless positional information implicit in the location of the dots is lost while the meaningful information (the number of dots within a region) remains constant. In the DEM, animation uses a random field as an error surface, based upon the root mean squared error (RMSE). The amount of error at a location is constantly changed, giving no impression that the elevation is precisely known. The ability to vary the spatial autocorrelation within the error field provides a graphic illustration that the usual RMSE is not a sufficient method for the reporting of error in spatial databases. In both examples animation of reliability is believed to make a novel, but appropriate, use of the computer in cartography.     

BinSq: visualizing geographic dot density patterns with gridded maps

ABSTRACT

Dot maps have become a popular way to visualize discrete geographic data. Yet, beyond showing how the data are spatially distributed, dot maps are often visually cluttered in terms of consistency, overlap, and representativeness. Existing clutter reduction techniques like jittering, refinement, distortion, and aggregation also address this issue, but do so by arbitrarily displacing dots from their exact location, removing dots from the map, changing the spatial reference of the map, or reducing its level of detail, respectively. We present BinSq, a novel visualization technique to compare variations in dot density patterns without visual clutter. Based on a careful synthesis of existing clutter reduction techniques, BinSq reduces the wide variety of dot density variations on the map to a representative subset of density intervals that are more distinguishable. The subset is derived from a nested binning operation that introduces order and regularity to the map. Thereafter, a dot prioritization operation improves the representativeness of the map by equalizing visible data values to correspond with the actual data. In this paper, we describe the algorithmic implementation of BinSq, explore its parametric design space, and discuss its capabilities in comparison to six existing clutter reduction techniques for dot maps.     

Creating a Dot Density Map: Resident Population in Mainland Portugal

Dot maps are one of the best ways to visualize absolute values in thematic cartography. Dots represent quantitative data on a map. Population is often used in this type of representation. This paper presents a population dot density map for the year 2011 on two scales: (1) for mainland Portugal, and (2) for the Lisbon and Oporto regions. We have used dots with constant values and sizes at the most detailed statistical level (i.e. statistical subsection) for localities with less than 5000 inhabitants, and proportional circles for localities with more than 5000 inhabitants. These two scales of analysis coupled with two cartographic representation techniques used on a single map allow for a clear reading of the distribution of population.     

Exploring nationally and regionally defined models for large area population mapping

Interactions between humans, diseases, and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation datasets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.     

一种加网图像去网的算法

采用查找网点中心的方法，计算出任意加网图像的网点网格，按网格中网点的面积率来转换成灰度值，改变二值图属性，从而达到去网的目的。实验结果表明，去网效果与加网图像的分辨率、加网线数、加网角度等因素有关。使用此算法可以得到较满意的去网效果，可以合成出满足打样要求的彩色图像，用于RIP后图像的数字打样。     

Dasymetric Estimation of Population Density and Areal Interpolation of Census Data

This paper describes techniques to compute and map dasymetric population densities and to areally interpolate census data using dasymetrically derived population weights. These techniques are demonstrated with 1980-2000 census data from the 13-county Atlanta metropolitan area. Land-use/land-cover data derived from remotely sensed satellite imagery were used to determine the areal extent of populated areas, which in turn served as the denominator for dasymetric population density computations at the census tract level. The dasymetric method accounts for the spatial distribution of population within administrative areas, yielding more precise population density estimates than the choroplethic method, while graphically representing the geographic distribution of populations. In order to areally interpolate census data from one set of census tract boundaries to another, the percentages of populated areas affected by boundary changes in each affected tract were used as adjustment weights for census data at the census tract level, where census tract boundary shifts made temporal data comparisons difficult. This method of areal interpolation made it possible to represent three years of census data (1980, 1990, and 2000) in one set of common census tracts (1990). Accuracy assessment of the dasymetrically derived adjustment weights indicated a satisfactory level of accuracy. Dasymetrically derived areal interpolation weights can be applied to any type of geographic boundary re-aggregation, such as from census tracts to zip code tabulation areas, from census tracts to local school districts, from zip code areas to telephone exchange prefix areas, and for electoral redistricting.     

Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009

ABSTRACT

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.     

Categories are in flux,but their computational representations are fixed: That's a problem

As research advances, our conceptual understanding also changes. Computational approaches do little to recognize the evolution that occurs at the conceptual level during the research process. This can result in misunderstanding between knowledge producers and consumers and so inhibit the reusability of outcomes. In this article, we describe how changes at the conceptual level can be represented, along with related changes to data and methods, and how appropriate connections between these various artefacts can be maintained. To demonstrate these ideas, we show how categories used in remote sensing and land cover analysis change over time and how these changes are linked to various research activities. We present a new system (called AdvoCate) that augments typical GIS and remote sensing functionality with a conceptual model of categories that can undergo change, and that also captures the cause of conceptual change and its extent. We argue that concepts and categories should be represented explicitly and richly within GIS, because without this, we have a poor idea of what our modeled entities really mean, and by implication how they should be used appropriately. We demonstrate the usefulness of this deeper representation using examples of category evolution from a land cover mapping exercise.     

无锡市水利普查成果展示系统的设计与实现

全国第一次水利普查完成数据成果汇总后,成果展示及开发应用相即展开。无锡市水利局结合自身工作及管理需要,构建了完善且适合本地需要的水利空间成果库和属性库,以地理信息技术为支撑,实现了无锡市水利普查成果"一张图"展示。无锡市水利普查成果展示系统建设以水利管理部门需求为依托,分析各项管理工作对地理信息系统提出的功能需求,系统实现对无锡市河湖水系、水利设施等进行定位、搜索、属性信息存取与分析处理等。使水利普查成果成为动态服务地图,既方便使用者查看,也便于分析水利要素周围情况及合理性,同时为水利管理工作人员规划决策提供依据。本系统将无锡市水利普查成果应用到实际工作中,为无锡市水利信息化服务,使水利普查成果为民所用。     

Toward green cartography & visualization: a semantically-enriched method of generating energy-aware color schemes for digital maps

ABSTRACT

We introduce a semantically-enriched method of generating color schemes for various types of digital maps that reduces the energy consumption of the display device while preserving the quality of the original design. Energy-aware design intersects two important trends in cartography. First, as more maps are viewed today on mobile, battery life has become a central constraint influencing design. Second, there is increasing need for green computing, which encourages the efficient use of energy to limit environmental impacts. This paper focuses on one important aspect of energy-aware cartography: color design. Existing research on energy-aware color adjustment methods apply broadly to images or websites. However, the colors used in maps have more structured semantic relationships than most documents viewed on mobile devices, and efforts to account for these relationships while reducing energy consumption are limited. To fill this gap, we mathematically formalize energy-aware map-color adjustment as a constrained optimization problem: we define energy consumption as the objective function and model the preservation of semantic relationships as the search constraints. We evaluate our proposed method against a common color dimming method using four maps with different semantic relationships. The evaluation suggests that our proposed method better preserves the original color semantics.     

DTP系统环境下地图的制作与出版

彩色地图的出版印刷和一般的彩色图文的出版印刷有很多相似之处 ,但也有不同之处。本实验选择了一幅有代表性的地图 ,图内要素不仅包括水系 ,道路 ,居民地和等高线 ,而且还有分层设色 ,图外还有彩色附图和文字说明。尝试运用常用桌面出版系统制作 ,直接得到 5张 A4幅面的分色晒版胶片 (黄、品红、青、黑和棕专色 )。实验结果证明 ,桌面出版系统软件功能较全面 ,可以制作和出版地图 ,尤其是适合制作和出版带有广告 ,彩色图片的专题地图和影像地图     

Using Semi-variance Image Texture Statistics to Model Population Densities

This study presents a method to model population densities by using image texture statistics of semi-variance. In a case study of the City of Austin, Texas, we first selected sample census blocks of the same land use to build population models by land use. Regression analyses were conducted to infer the relationship between block population densities and image texture statistics of the semi-variance. We then applied the population models to an area of 251 blocks to estimate populations for within-blocks land-use areas while maintaining census block populations. To assess the proposed method, the same analysis was performed while census block-group populations were maintained, and the aggregated block populations were compared with original census block populations. We also tested a conventional land-use-based dasymetric mapping method with pre-calculated population densities for land uses. The results show that our approach, which is based on initial land-use stratification and further image-texture statistical modeling of population, has higher accuracy statistics than the conventional land-use-based dasymetric mapping method.     

A CARTOGRAPHIC SIMULATION MODEL OF THE EFFECTS OF ECONOMIC ACTIVITY ON THE ENVIRONMENT

The principles of systems mapping are employed in a model simulating the effects of human activity on the environment. The model takes the form of series of maps which are organized within three basic units or “blocks.” Block I contains general preliminary maps of land use and environmental change within an area which can serve as a basis for subsequent forecasting and proposals for improving land use. Block II contains maps that take stock of, evaluate, and predict human impacts on specific environmental components, and show the spatial distribution of measures proposed to ameliorate these impacts. Block III consists of maps synthesizing the component-related information of the preceding block for the purpose of providing summary assessments about the condition of the environment and land use conflicts in an area and targeting areas deserving the most comprehensive reclamation efforts. Methods of compiling the various maps are described in detail. Translated from: Izvestiya AN SSSR, seriya geograficheskaya, 1986, No. 2, pp. 114–127.     

利用泰森多边形和格网平滑的人口密度空间化研究——以徐州市为例

在运用泰森多边形作边界替代得到徐州市区人口密度的分布图后,采用GCAWI法实现了徐州市区人口数据的空间化,提出了采用大小两种格网错置的平滑方法,并运用此方法得到徐州市2000年和2005年的500 m×500 m的格网人口密度分布图。结果表明:①通过对比两个年份的人口密度分布图可以发现,徐州市区人口密度在2000年~2005年期间具有南移东扩的趋势;②利用大小两种格网错置平滑的方法实现了对人口密度空间化与平滑两个功能,平滑后的结果表明,徐州市区人口密度的空间分布具有块状集聚、轴状延伸的格局,整体来看呈现出"大"字形的态势;③通过与直接运用核函数内插得到的人口密度分布图的对比可以发现,前者对反映人口密度分布的细部差异比较敏感,而后者仅能反映出人口密度空间分布的整体格局。     

Scree Representation on Topographic Maps

Abstract

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Scree patterns are an important element of mountain maps in Swiss style. The size and density of scree dots vary with the exposition towards a source of illumination, which makes the dots extremely labour intensive to map without specialized algorithms. This paper identifies design principles for the symbolisation of scree fields on mountain slopes and presents a digital method for the quick placement of dot symbols requiring only minimal interventions by a cartographer. When digitally produced scree is combined with a shaded relief and a rock drawing, the terrain appears as a continuous three-dimensional surface to the reader. The described method is implemented in Scree Painter, a specialized free open-source software application. Scree patterns produced with Scree Painter match the quality standards of manually generated scree representations.     

POPULATION CENSUS WITH THE AID OF AERIAL PHOTOGRAPHS: AN EXPERIMENT IN THE CITY OF LEEDS

This study shows how aerial photographs can be of value in a population census. The census and the enumeration district maps were used initially to obtain population data and the housing stock was derived from the aerial photographs. From these the population densities were determined of a number of sample enumeration districts containing a single type of house. Another set of enumeration districts was selected and the housing stock again derived from the aerial photographs. By considering the type and quantity of housing stock and the population density of each housing type, the population figures were estimated for each enumeration district. The values of these population estimates were then compared with the values recorded in the census. The overall population estimate had an error of only 2%, but the estimates for some of the individual enumeration districts showed greater errors. These errors are assessed and analysed and some suggestions are made to improve the methodology used in this study.     

Improving Large Area Population Mapping Using Geotweet Densities

Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo‐located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo‐located tweets in 1x1 km grid cells over a 2‐month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests‐based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media‐derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.     

Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

ABSTRACT

Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods. These advances in urban feature extraction and built-area detection can refine the mapping of human population densities, especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data. However, in these contexts it is unclear how best to use built-area data to disaggregate areal, count-based census data. Here we tested two methods using remotely sensed, built-area land cover data to disaggregate population data. These included simple, areal weighting and more complex statistical models with other ancillary information. Outcomes were assessed across eleven countries, representing different world regions varying in population densities, types of built infrastructure, and environmental characteristics. We found that for seven of 11 countries a Random Forest-based, machine learning approach outperforms simple, binary dasymetric disaggregation into remotely-sensed built areas. For these more complex models there was little evidence to support using any single built land cover input over the rest, and in most cases using more than one built-area data product resulted in higher predictive capacity. We discuss these results and implications for future population modeling approaches.     

Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice

ABSTRACT

Data on global population distribution are a strategic resource currently in high demand in an age of new Development Agendas that call for universal inclusiveness of people. However, quality, detail, and age of census data varies significantly by country and suffers from shortcomings that propagate to derived population grids and their applications. In this work, the improved capabilities of recent remote sensing-derived global settlement data to detect and mitigate major discrepancies with census data is explored. Open layers mapping built-up presence were used to revise census units deemed as ‘unpopulated’ and to harmonize population distribution along coastlines. Automated procedures to detect and mitigate these anomalies, while minimizing changes to census geometry, preserving the regional distribution of population, and the overall counts were developed, tested, and applied. The two procedures employed for the detection of deficiencies in global census data obtained high rates of true positives, after verification and validation. Results also show that the targeted anomalies were significantly mitigated and are encouraging for further uses of free and open geospatial data derived from remote sensing in complementing and improving conventional sources of fundamental population statistics.