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.     

Geo-questionnaires in urban planning: recruitment methods,participant engagement,and data quality

ABSTRACT

Recent focus on sustainable urban development and livability has increased the demand for new data sourcing techniques to capture experiences and preferences of urban dwellers. At the same time, developments of geospatial technologies and social media have enabled new types of user-generated geographic information and spatially explicit online communication. As a result, new public participation GIS methods for engaging large groups of individuals have emerged. One such method is geo-questionnaire, an online questionnaire with mapping capabilities, which has been used to elicit geographic data in variety of topics and geographical contexts. This article presents two recent cases, in which geo-questionnaires have been used in Polish cities to obtain public input on quality of life and development preferences in local land use planning. The article evaluates participant recruitment methods focusing on sample representativeness, participant engagement, and data quality. Recruitment via social media was found to increase bias towards younger population. Paper questionnaires used along the online version provided for better representation of target population’s age structure, but did not reduce bias related to educational attainment. We discuss how these issues relate to data usability and generalizability in the context of digital divide, and suggest directions for future research.     

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.     

A multilevel stratified spatial sampling approach based on terrain knowledge for the quality assessment of OpenStreetMap dataset in Hong Kong

With the development of Volunteered Geographical Information (VGI) data, the OpenStreetMap has high research value in terms of project activity, social influence, urban development, application scope, and historical richness and the number of buildings or roads is increasing every day. However, how to evaluate the quality of a large amount OpenStreetMaps efficiently and accurately is still not fully understood. This article presents the development of an approach regarding multilevel stratified spatial sampling based on slope knowledge and official 1:1000 thematic maps as the reference dataset for OpenStreetMap data quality inspection of Hong Kong. This multilevel stratified spatial sampling plan is as follows: (1) The terrain characteristics of Hong Kong are fully considered by dividing grids into quality estimate strata based on the slope information; (2) Spatial sampling for the selection of grids or objects is used; (3) A more reliable sampling subset is made, regarding the representation of the entire OpenStreetMap dataset of Hong Kong. This sampling plan displays a 10% higher sampling accuracy, but without increasing the sample size, particularly as regards building completeness inspection compared with simple random sampling and systematic random sampling. This research promotes further applications of the Open-Street-Map dataset, thus enabling us to have a better understanding of the OpenStreetMap data quality in urban areas.     

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.     

Spatial analysis using clustering methods: Evaluating central point and median approaches

Spatial analysis is an important area of research which continues to make major contributions to the exploratory capabilities of geographical information systems. The use and application of classic clustering methods is being pursued as an exploratory approach for the analysis of spatially referenced data. Numerous potential clustering approaches exist, so research assessing the relative differences of these approaches is important. This paper evaluates the median and central points optimization based clustering approaches for use in the context of exploratory spatial data analysis. Functional and visual comparisons using three spatial applications across a range of cluster values are carried out. The empirical results suggest that these two clustering approaches identify very similar groupings. The significance of this finding is that the development of clustering tools for exploratory analysis may be limited to the median based approach given relative computational and solvability considerations. Received: 28 September 1998/Accepted: 9 August 1999     

Dynamic spatial panels: models, methods, and inferences

This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent variable lagged in space, a dependent variable lagged in both space and time, independent variables lagged in time, independent variables lagged in space, serial error autocorrelation, spatial error autocorrelation, spatial-specific and time-period-specific effects. The survey also examines the reasoning behind different model specifications and the purposes for which they can be used, which should be useful for practitioners.     

Effects of vegetation cover on summer climate in China during 1982–2001

In this paper, we apply lagged correlation analysis to study the effects of vegetation cover on the summer climate in different zones of China, using NOAA/AVHRR normalized difference vegetation index (NDVI) data during the time period from 1982 to 2001 and climate data of 365 meteorological stations across China (precipitation from 1982 to 2001 and temperature from 1982 to 1998). The results show that there are positive correlations between spring NDVI and summer climate (temperature and precipitation) in most zones of China; these suggest that, when the vegetation cover increases, the summer precipitation will increase, and the lagged correlations show a significant difference between zones. The stronger correlations between NDVI in previous season and summer climate occur in three zones (Mid-temperate zone, Warm-temperate zone and Plateau climate zone), and this implies that vegetation changes have more sensitive feedback effects on climate in the three zones in China.     

矢量图形数据的空间数据库存取方法的研究

用现代数据库统一来存放和管理空间数据与属性数据是GIS的发展趋势：利用Oracle数据库的Spatial模块,可以进行空间几何数据的相关存取,方便地实现对空间几何数据的管理、通过开发基于J2EE构架的Oracle数据库应用程序,实现了矢量图形数据的存取,提出了一种开发GIS矢量图形系统的新思路     

Conjugate ruptures and seismotectonic implications of the 2019 Mindanao earthquake sequence inferred from Sentinel-1 InSAR data

In 2019, four strong earthquakes of Mw>6.4 occurred successively in Mindanao, Philippines. Based on the reports from the USGS and PHIVOLCS, these earthquakes were dominated by strike-slip ruptures. Whether these earthquakes are temporally and spatially related remained unknown. We characterized the coseismic displacement fields during the earthquake sequence using an InSAR technique with Sentinel-1 SAR data. The InSAR deformation measurements convincingly reveal that the four earthquakes produced distinct coseismic displacement patterns. We estimated the source parameters of the earthquakes with a two-step inversion strategy. The optimal model suggests that the earthquake sequence resulted from the reactivation of a conjugate fault structure that involves two nearly vertical left-lateral strike-slip faults and two high-angle right-lateral strike-slip faults. We calculated Coulomb stress changes from the earthquake sequence, suggesting that the previous strong earthquakes had significant stress-encouraging effects on the following events. The regional velocities based on the GPS analysis suggest that the formation of this conjugate structure is mainly due to the westward movement of the subducting Philippine Sea Plate. This earthquake sequence provides a seismotectonic background for subsequent strong earthquakes and helps to better understand the formation mechanisms and seismotectonic implications of conjugate structure rupturing.     

Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of central Asia

We analyzed spatially averaged normalized difference vegetation index (NDVI) time series from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset of 11 desert and semidesert ecoregions in central Asia using standard statistical tests for discontinuities and trends. Results from the test for discontinuities reveal that seven ecoregions display significant differences in the data acquired by the AVHRRs on the National Oceanic and Atmospheric Administration satellite 11 (NOAA-11) versus the data acquired by AVHRR on other NOAA satellites (NOAA-7, NOAA-9, and NOAA-14). Across the more than 2/spl times/10/sup 6/ km/sup 2/ of deserts and semideserts in the selected central Asian ecoregions, a significant upward trend in NDVI is evident during the tenure of NOAA-11 (1989-1994). This trend is not found during any other period. We argue that the data from the PAL NDVI dataset for NOAA-11 will pose problems for land surface change analyses, if these significant sensor-related artifacts are ignored. We do not find these artifacts in data from the other three satellites (NOAA-7, NOAA-9, and NOAA-14). We suggest that the comparison of data from any combination of these three AVHRRs can be used for land surface change analyses, but that the inclusion of NOAA-11 AVHRR NDVI data in trend analyses may result in the detection of spurious trends.     

基于ArcGIS的标准分幅裁切新方法

数据裁切是数据生产过程中相当重要的一个环节,也是耗时较长的一道工序。本文介绍了一种基于ArcGIS的简单实用的数据裁切新方法,可以进行批量生产,具有快捷、简单、实用的特点。     

Area-based and location-based validation of classified image objects

Geographic object-based image analysis (GEOBIA) produces results that have both thematic and geometric properties. Classified objects not only belong to particular classes but also have spatial properties such as location and shape. Therefore, any accuracy assessment where quantification of area is required must (but often does not) take into account both thematic and geometric properties of the classified objects. By using location-based and area-based measures to compare classified objects to corresponding reference objects, accuracy information for both thematic and geometric assessment is available. Our methods provide location-based and area-based measures with application to both a single-class feature detection and a multi-class object-based land cover analysis. In each case the classification was compared to a GIS layer of associated reference data using randomly selected sample areas. Error is able to be pin-pointed spatially on per-object, per class and per-sample area bases although there is no indication whether the errors exist in the classification product or the reference data. This work showcases the utility of the methods for assessing the accuracy of GEOBIA derived classifications provided the reference data is accurate and of comparable scale.     

Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes

In this paper, we extend the applicability of a previously proposed class of dynamic space-time models by enabling them to accommodate large datasets. We focus on the common setting where space is viewed as continuous but time is taken to be discrete. Scalability is achieved by using a low-rank predictive process to reduce the dimensionality of the data and ease the computational burden of estimating the spatio-temporal process of interest. The proposed models are illustrated using weather station data collected over the northeastern United States between 2000 and 2005. Here our interest is to use readily available predictors, association among measurements at a given station, as well as dependence across space and time to improve prediction for incomplete station records and locations where station data does not exist.     

Spatially constrained regionalization with multilayer perceptron

In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non‐stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity‐constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance.     

Catchment infiltration I: Distribution of variables

Environmental modelling usually requires spatially distributed inputs for model operation. We propose that such inputs are best obtained from field measured data. Geographic information systems (GIS) provide a logical framework to distribute measured inputs spatially, to manipulate ensuing data fields during analysis, and to display the results. This paper describes a study conducted on a 123 km² catchment in Pennsylvania. The purpose was to evaluate how spatial variability of macroporosity affects distribution of other infiltration-related parameters. We measured sorptivity, conductivity and macroporosity at specific points within a catchment, and interpolated their spatial distributions by kriging. The measurements were made with ring and disk infiltrometers, sampling locations were geo-referenced with a global positioning system (GPS), and data were analysed using geostatistical techniques in a GIS context. Field values ( hard data ) were supplemented by soft data derived from cumulative distribution functions (cdfs) and available soil maps. Results showed that, when spatial variability associated with macroporosity was removed, infiltration parameters became less variable. Observed correlation among measured parameters suggested a form of potential transfer functions. We conclude that infiltration can be modelled at either the farm or catchment scale if macroporosity and spatial variability of infiltration parameters are adequately defined, and we suggest approaches which can be used in a GIS context to attain that goal.     

Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated.We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites.Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively.Evaluating classification models with different degrees of spatial correlation between training and validation data was shown to give highly different prediction accuracies, thereby highlighting model transferability and application potential. Aerial image and DEM-based RF models had low transferability to new areas due to lack of representation of aerial image, landscape and vegetation variation in training data. They do, however, show promise at local scale for supporting conservation and management with vegetation mappings of high spatial and thematic detail based on low-cost image data.     

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.      

尺度分层多阈值结合SVM分类的热红外舰船识别

针对尺度自适应选择分层多阈值方法,有时检测目标不完整且存在较多虚警目标等问题,提出了一种基于尺度分层多阈值和SVM分类相结合的舰船目标检测与识别方法。首先使用尺度自适应分层多阈值方法进行初检测;其次根据样本学习生成舰船目标特征及最佳分类特征组合;最后使用SVM样本学习分类实现舰船目标检测与识别。实验结果表明,该方法比单一使用样本分类法降低了虚警率,提高了检测效率,能在近岸舰船目标与背景对比度较低的情况下实现虚假目标有效剔除,且在突堤式码头停放的舰船目标识别中更有效和更稳定。