Spatially simplified scatterplots for large raster datasets

Abstract

Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.     

Analysis of human spatial behavior in a GIS environment: Recent developments and future prospects

In recent years, comprehensive geographic data sets of metropolitan areas and individual-level, georeferenced data are becoming more available to social scientists. At the same time, tools for performing spatial analysis in a GIS environment have also become more available. These developments provide many new opportunities for the analysis and theoretical understanding of disaggregate human spatial behavior. This paper examines how these developments may enable the researcher to represent complex urban and cognitive environments more realistically, and to overcome the limitations of aggregate spatial data framework. It explores their implications for the theoretical and methodological development in geography and other social science disciplines.     

Modeling spatial dependence in high spatial resolution hyperspectral data sets

 As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem. Received: 25 February 2001 / Accepted: 2 August 2001     

Geolokit: An interactive tool for visualising and exploring geoscientific data in Google Earth

Virtual globes have been developed to showcase different types of data combining a digital elevation model and basemaps of high resolution satellite imagery. Hence, they became a standard to share spatial data and information, although they suffer from a lack of toolboxes dedicated to the formatting of large geoscientific dataset. From this perspective, we developed Geolokit: a free and lightweight software that allows geoscientists – and every scientist working with spatial data – to import their data (e.g., sample collections, structural geology, cross-sections, field pictures, georeferenced maps), to handle and to transcribe them to Keyhole Markup Language (KML) files. KML files are then automatically opened in the Google Earth virtual globe and the spatial data accessed and shared. Geolokit comes with a large number of dedicated tools that can process and display: (i) multi-points data, (ii) scattered data interpolations, (iii) structural geology features in 2D and 3D, (iv) rose diagrams, stereonets and dip-plunge polar histograms, (v) cross-sections and oriented rasters, (vi) georeferenced field pictures, (vii) georeferenced maps and projected gridding.Therefore, together with Geolokit, Google Earth becomes not only a powerful georeferenced data viewer but also a stand-alone work platform. The toolbox (available online at http://www.geolokit.org) is written in Python, a high-level, cross-platform programming language and is accessible through a graphical user interface, designed to run in parallel with Google Earth, through a workflow that requires no additional third party software. Geolokit features are demonstrated in this paper using typical datasets gathered from two case studies illustrating its applicability at multiple scales of investigation: a petro-structural investigation of the Ile d’Yeu orthogneissic unit (Western France) and data collection of the Mariana oceanic subduction zone (Western Pacific).     

GIS-based Indicators of Montana Grasshopper Communities

We evaluated two digital data sources that might be helpful in characterizing grasshopper habitat using plant and grasshopper species composition data collected at 128 sites in three areas of Montana. A GIS was used to associate each sampling site with Omernik's ecoregions and the Montana State Soil Geographic Database (MTSTATSGO). Detrended Correspondence Analysis (DCA) and statistical analyses were used to test for correlations among grasshopper species, available water capacity, and soil permeability across sampling areas and ecoregions. Four grasshopper species were correlated with soil permeability and six were correlated with available water capacity. MTSTATSGO plant cover percentages did not correlate with cover measured in the field, indicating inadequate resolution for the scale of this study. Ecoregions were useful in distinguishing grasshopper community gradients across Montana, from mountains to plains. These georeferenced data should be considered as input for grasshopper forecasting and decision-making models. Our results show how GIS can be used to evaluate relationships between digital data sets and ecological data gathered in the field.     

Accessibility tradeoffs in public transit planning

 Spatial accessibility is a critical consideration in the provision of services, both public and private. In public transit planning, accessibility is comprised of access and geographic coverage. Interestingly, these two considerations are somewhat at odds with each other. Access is important because it is the process associated with getting to and departing from the service. Such access is typically perceived of in spatial terms as the physical proximity to transit stops or stations. Additional stops along a route usually mean greater access, because a stop is more likely to be within an acceptable walking/driving standard for a larger number of people. On the other hand, more stops and greater access slow transit travel speeds, thereby decreasing the area of service reachable given a travel time budget. More stops along a route translate to greater service interruption and longer travel times. The faster the travel time, the more desirable the service. Further, if travel times become excessive, then user demand for service will decrease. All of this means that stop spacing along a route is central to accessibility, as it is a tradeoff of access (more stops) and geographic coverage (service efficiency through less stops). This paper details modeling approaches for addressing accessibility concerns in an integrated fashion. Bus-based transit service in Columbus, Ohio will be utilized to illustrate the usefulness of these approaches in transit planning. Received: September 2002 / Accepted: January 2003 This research was supported in part by an Ohio State University seed grant.     

The CNSDTF topological vector model and structure

Geo-Spatial Data Transfer Standard is an important part of “National Spatial Data Infrastructure (NSDI)”, as well as a necessary means for data sharing. “Chinese National Geo-Spatial Data Transfer Format (CNSDTF)” was approved by National Quality Technology Supervise Bureau in 1999 with the standard serial number of 17798-1999. It is designed to support vector and raster spatial data. This paper describes the vector part of CNSDTF, including design ideas, main characters, conceptual model, definition of spatial object, and file structure.     

The CNSDTF topological vector model and structure

Geo-Spatial Data Transfer Standard is an important part of "National Spatial Data Infrastructure(NSDI)" ,as well as a necessary means for data sharing. "Chinese National Geo-Spatial Data Transfer Format (CNSDTF)" was approved by National Quality Technology Supervise Bureau in 1999 with the standard serial number of 17798-1999. It is designed to support vector and raster spatial data. This paper describes the vector part of CNSDTF, including design ideas, main characters, conceptual model, definition of spatial object, and file structure.     

电子政务中基于XML的空间数据交换格式研究

电子政务应用中,常需要将不同来源的空间数据进行集成,基于XML的数据交换是对空间数据集成的有效方式之一。笔者参与了某电子政务空间辅助决策示范工程的空间数据交换格式研究。本文讨论电子政务中基于XML的空间数据交换格式的设计目标、遵循原则、数据模型和矢量数据交换格式中的关键点。最后给出一个建议的用于电子政务的空间数据交换格式的XMLSchema框图。并对地球信息交换格式国家标准提出了几点改进意见。     

Global and local spatial autocorrelation in bounded regular tessellations

This paper systematically investigates spatially autocorrelated patterns and the behaviour of their associated test statistic Moran's I in three bounded regular tessellations. These regular tessellations consist of triangles, squares, and hexagons, each of increasing size (n=64; 256; 1024). These tesselations can be downloaded at http://geo-www.sbs.ohio-state.edu/faculty/tiefelsdorf/regspastruc/ in several GIS formats. The selection of squares is particularly motivated by their use in raster based GIS and remote sensing. In contrast, because of topological correspondences, the hexagons serve as excellent proxy tessellations for empirical maps in vector based GIS. For all three tessellations, the distributional characteristics and the feasibility of the normal approximation are examined for global Moran's I, Moran's I ^(k) associated with higher order spatial lags, and local Moran's I _i. A set of eigenvectors can be generated for each tessellation and their spatial patterns can be mapped. These eigenvectors can be used as proxy variables to overcome spatial autocorrelation in regression models. The particularities and similarities in the spatial patterns of these eigenvectors are discussed. The results indicate that [i] the normal approximation for Moran's I is not always feasible; [ii] the three tessellations induce different distributional characteristics of Moran's I, and [iii] different spatial patterns of eigenvectors are associated with the three tessellations. Received: 2 July 1999 / Accepted: 9 November 1999     

人口统计数据的空间分布化研究

分析了传统的人口空间分布密度衰减函数－指数型和Gauss型，指出了其应用的局限性，对于有两个中心以上的城市，提出了将人口统计数据空间分布化的思路和方法。     

Interactive analysis of the spatial distribution of disease using a geographic information systems

Geographic information systems (GIS) provide a variety of tools for the manipulation and display of public health data. Few, however, enable users to interactively evaluate hypotheses on spatial trends in disease risk that may be suggested by maps of measures of disease impact. We addressed this limitation by developing a seamless interface between a commercial GIS and a suite of spatial analysis algorithms. Users of the system can utilize the GIS's capability to interactively select and manipulate geographically referenced data and, through a series of pull-down menus, apply a variety of exploratory analysis methods to this information. In the presented application, we illustrate this capability by including algorithms for the reduction of random noise in observed incidence rates, for the detection of unusual aggregations of disease events, and for the statistical evaluation of inferences drawn from spatial trends. We demonstrate this application by examining lung cancer mortality in the state of Ohio. Received: 22 September 1999 / Accepted: 8 March 2000     

A comprehensive framework for exploratory spatial data analysis: Moran location and variance scatterplots

Abstract

A significant Geographic Information Science (GIS) issue is closely related to spatial autocorrelation, a burning question in the phase of information extraction from the statistical analysis of georeferenced data. At present, spatial autocorrelation presents two types of measures: continuous and discrete. Is it possible to use Moran's I and the Moran scatterplot with continuous data? Is it possible to use the same methodology with discrete data? A particular and cumbersome problem is the choice of the spatial-neighborhood matrix (W) for points data. This paper addresses these issues by introducing the concept of covariogram contiguity, where each weight is based on the variogram model for that particular dataset: (1) the variogram, whose range equals the distance with the highest Moran I value, defines the weights for points separated by less than the estimated range and (2) weights equal zero for points widely separated from the variogram range considered. After the W matrix is computed, the Moran location scatterplot is created in an iterative process. In accordance with various lag distances, Moran's I is presented as a good search factor for the optimal neighborhood area. Uncertainty/transition regions are also emphasized. At the same time, a new Exploratory Spatial Data Analysis (ESDA) tool is developed, the Moran variance scatterplot, since the conventional Moran scatterplot is not sensitive to neighbor variance. This computer-mapping framework allows the study of spatial patterns, outliers, changeover areas, and trends in an ESDA process. All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb© (or, in the near future, myGeooffice.org).     

Big spatial data for urban and environmental sustainability

ABSTRACT

Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

THE DEPARTMENT OF PHOTOGRAMMETRY AND REMOTE SENSING AT WUHAN TECHNICAL UNIVERSITY,PEOPLE'S REPUBLIC OF CHINA

Many remote sensing applications are predicated on the fact that there is a known relationship between climate and vegetation dynamics as monitored from space. However, few studies investigate vegetation index variation on individual homogeneous land cover units as they relate to specific climate and environmental influences at the local scale. This study focuses on the relationship between the Palmer Drought Severity Index (PDSI) and different vegetation types through the derivation of vegetation indices from Landsat 7 ETM+ data (NDVI, Tasseled Cap, and SAVI). A series of closely spaced through time images from 1999 to 2002 were selected, classified, and analyzed for an area in northeastern Ohio. Supervised classification of the images allowed us to monitor the response in individual land cover classes to changing climate conditions, and compare these individual changes to those over the entire larger areas. Specifically, the images were compared using linear regression techniques at various time lags to PDSI values for these areas collected by NOAA. Although NDVI is a robust indicator of vegetation greenness and vigor, it may not be the best index to use, depending on the type of vegetation studied and the scale of analysis used. A combination of NDVI and other prominent vegetation indices can be used to detect subtle drought conditions by specifically identifying various time lags between climate condition and vegetation response.     

Impacts of Positional Error on Spatial Regression Analysis: A Case Study of Address Locations in Syracuse, New York

Positional error is the error produced by the discrepancy between reference and recorded locations. In urban landscapes, locations typically are obtained from global positioning systems or geocoding software. Although these technologies have improved the locational accuracy of georeferenced data, they are not error free. This error affects results of any spatial statistical analysis performed with a georeferenced dataset. In this paper we discuss the properties of positional error in an address matching exercise and the allocation of point locations to census geography units. We focus on the error's spatial structure, and more particularly on impacts of error propagation in spatial regression analysis. For this purpose we use two geocoding sources, we briefly describe the magnitude and the nature of their discrepancies, and we evaluate the consequences that this type of locational error has on a spatial regression analysis of pediatric blood lead data for Syracuse, NY. Our findings include: (1) the confirmation of the recurrence of spatial clustering in positional error at various geographic resolutions; and, (2) the identification of a noticeable but not shockingly large impact from positional error propagation in spatial auto‐binomial regression analysis results for the dataset analyzed.