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Abstract Nighttime lights (NTL) imagery is one of the most commonly used tools to quantitatively study socioeconomic systems over large areas. In this study we aim to use location-based social media big data to challenge the primacy of NTL imagery on estimating socioeconomic factors. Geo-tagged tweets posted in the contiguous United States in 2013 were retrieved to produce a tweet image with the same spatial resolution of the NTL imagery (i.e., 0.00833° × 0.00833°). Sum tweet (the total number of tweets) and sum light (summed DN value of the NTL image) of each state or county were obtained from the tweets and the NTL images, respectively, to estimate three important socioeconomic factors: personal income, electric power consumption, and fossil fuel carbon dioxide emissions. Results show that sum tweet is a better measure of personal income and electric power consumption while carbon dioxide emissions can be more accurately estimated by sum light. We further exploited that African-Americans adults are more likely than White seniors to post geotagged tweets in the US, yet did not find any significant correlations between proportions of the subpopulations and the estimation accuracy of the socioeconomic factors. Existence of saturated pixels and blooming effects and failure to remove gas flaring reduce quality of NTL imagery in estimating socioeconomic factors, however, such problems are nonexistent in the tweet images. This study reveals that the number of geo-tagged tweets has great potential to be deemed as a substitute of brightness of NTL to assess socioeconomic factors over large geographic areas. [ABSTRACT FROM AUTHOR]

The environmental drivers and mechanisms of influenza dynamics remain unclear. The recent development of influenza surveillance--particularly the emergence of digital epidemiology--provides an opportunity to further understand this puzzle as an area within applied human biometeorology. This paper investigates the short-term weather effects on human influenza activity at a synoptic scale during cold seasons. Using 10 years (2005-2014) of municipal level influenza surveillance data (an adjustment of the Google Flu Trends estimation from the Centers for Disease Control's virologic surveillance data) and daily spatial synoptic classification weather types, we explore and compare the effects of weather exposure on the influenza infection incidences in 79 cities across the USA. We find that during the cold seasons the presence of the polar [i.e., dry polar (DP) and moist polar (MP)] weather types is significantly associated with increasing influenza likelihood in 62 and 68% of the studied cities, respectively, while the presence of tropical [i.e., dry tropical (DT) and moist tropical (MT)] weather types is associated with a significantly decreasing occurrence of influenza in 56 and 43% of the cities, respectively. The MP and the DP weather types exhibit similar close positive correlations with influenza infection incidences, indicating that both cold-dry and cold-moist air provide favorable conditions for the occurrence of influenza in the cold seasons. Additionally, when tropical weather types are present, the humid (MT) and the dry (DT) weather types have similar strong impacts to inhibit the occurrence of influenza. These findings suggest that temperature is a more dominating atmospheric factor than moisture that impacts the occurrences of influenza in cold seasons. [ABSTRACT FROM AUTHOR]

The wide penetration of location-aware mobile devices and location-based services renders the location-based social media as a reliable proxy to study the real-world geographic space. Language diversity is an important indicator of a city's internationalization level. People communicate using different languages in the cyberspace of social media as they do in the geographic space. The location-based social media therefore provides an innovative set of lens to map the language diversity and study the internationalization of cities. In the enclosed graphics, based on a collection of geo-tagged Twitter posts, we generated a fine resolution map of language diversity index in the area of Hong Kong to illustrate the potential of location-based social media in city research. [ABSTRACT FROM AUTHOR]

Characterizing human mobility patterns is essential for understanding human behaviors and the interactions with socioeconomic and natural environment, and plays a critical role in public health, urban planning, transportation engineering and related fields. With the widespread of location-aware mobile devices and continuing advancement of Web 2.0 technologies, location-based social media (LBSM) have been gaining widespread popularity in the past few years. With an access to locations of hundreds of million users, profiles and the contents of the social media posts, the LBSM data provided a novel modality of data source for human mobility study. By exploiting the explicit location footprints and mining the latent demographic information implied in the LBSM data, the purpose of this paper is to investigate the spatiotemporal characteristics of human mobility with a particular focus on the impact of demography. To serve this purpose, we first collect geo-tagged Twitter feeds posted in the conterminous United States area, and organize the collection of feeds using the concept of space-time trajectory corresponding to each Twitter user. Commonly human mobility measures, including detected home and activity centers, are derived for each user trajectory. We then select a subset of Twitter users that have detected home locations in the city of Chicago as a case study, and apply name analysis to the names provided in user profiles to learn the implicit demographic information of Twitter users, including race/ethnicity, gender and age. Finally we explore the spatiotemporal distribution and mobility characteristics of Chicago Twitter users, and investigate the demographic impact by comparing the differences across three demographic dimensions (race/ethnicity, gender and age). We found that, although the human mobility measures of different demographic groups generally follow the generic laws (e.g., power law distribution), the demographic information, particular the race/ethnicity group, significantly affects the urban human mobility patterns. [ABSTRACT FROM AUTHOR]

With rapid advances of geospatial technologies, the amount of spatial data has been increasing exponentially over the past few decades. Usually collected by diverse source providers, the available spatial data tend to be fragmented by a large variety of data heterogeneities, which highlights the need of sound methods capable of efficiently fusing the diverse and incompatible spatial information. Within the context of spatial prediction of categorical variables, this paper describes a statistical framework for integrating and drawing inferences from a collection of spatially correlated variables while accounting for data heterogeneities and complex spatial dependencies. In this framework, we discuss the spatial prediction of categorical variables in the paradigm of latent random fields, and represent each spatial variable via spatial covariance functions, which define two-point similarities or dependencies of spatially correlated variables. The representation of spatial covariance functions derived from different spatial variables is independent of heterogeneous characteristics and can be combined in a straightforward fashion. Therefore it provides a unified and flexible representation of heterogeneous spatial variables in spatial analysis while accounting for complex spatial dependencies. We show that in the spatial prediction of categorical variables, the sought-after class occurrence probability at a target location can be formulated as a multinomial logistic function of spatial covariances of spatial variables between the target and sampled locations. Group least absolute shrinkage and selection operator is adopted for parameter estimation, which prevents the model from over-fitting, and simultaneously selects an optimal subset of important information (variables). Synthetic and real case studies are provided to illustrate the introduced concepts, and showcase the advantages of the proposed statistical framework. [ABSTRACT FROM AUTHOR]

Li and Zhang (2012b, Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatics) raised a series of comments on our recent paper (Cao, G., Kyriakidis, P.C., and Goodchild, M.F., 2011. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791), which include a notation error in the model equation provided for the Markov chain random field (MCRF) or spatial Markov chain model (SMC), originally proposed by Li (2007b, Markov chain random fields for estimation of categorical variables. Mathematical Geology, 39 (3), 321–335), and followed by Allard et al. (2011, An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393) about the misinterpretation of MCRF (or SMC) as a simplified form of the Bayesian maximum entropy (BME)-based approach, the so-called Markovian-type categorical prediction (MCP) (Allard, D., D'Or, D., and Froideveaux, R., 2009. Estimating and simulating spatial categorical data using an efficient maximum entropy approach. Avignon: Unite Biostatisque et Processus Spatiaux Institute National de la Recherche Agronomique. Technical Report No. 37; Allard, D., D'Or, D., and Froideveaux, R., 2011. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393). Li and Zhang (2012b, Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorial fields’ with communication on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science) also raised concerns regarding several statements Cao et al. (2011, Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791) had made, which mainly include connections between permanence of ratios and conditional independence, connections between MCRF and Bayesian networks and transiograms as spatial continuity measures. In this response, all of the comments and concerns will be addressed, while also communicating with Li and other colleagues on general topics in Markov chain geostatistics. [ABSTRACT FROM AUTHOR]

In this article, the prediction problem of categorical spatial data, that is, the estimation of class occurrence probability for (target) locations with unknown class labels given observed class labels at sample (source) locations, is analyzed in the framework of generalized linear mixed models, where intermediate, latent (unobservable) spatially correlated Gaussian variables (random effects) are assumed for the observable non-Gaussian responses to account for spatial dependence information. Within such a framework, a spatial multinomial logistic mixed model is proposed specifically to model categorical spatial data. Analogous to the dual form of kriging family, the proposed model is represented as a multinomial logistic function of spatial covariances between target and source locations. The associated inference problems, such as estimation of parameters and choice of the spatial covariance function for latent variables, and the connection of the proposed model with other methods, such as the indicator variants of the kriging family (indicator kriging and indicator cokriging) and Bayesian maximum entropy, are discussed in detail. The advantages and properties of the proposed method are illustrated via synthetic and real case studies. [ABSTRACT FROM AUTHOR]

Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation. [ABSTRACT FROM AUTHOR]

Understanding the spatial patterns of soil microbial communities and influencing factors is a prerequisite for soil health assessments and site-specific management to improve crop production. However, soil microbial community structure at the field scale is complicated by the interactions among topography and soil properties. The objectives of this study were to (1) characterize the spatial variability patterns of soil microbial communities at the field scale; (2) assess the influence of soil physico-chemical properties, topography and management on soil microbial biomass spatial variability. This study was conducted in a 194-ha commercially-managed field in Hale County, Texas, in 2017. A total of 212 composite soil samples were collected at 0–0.15 m depth and analyzed via the ester-linked fatty acid methyl ester (EL-FAME) method to characterize the microbial community structure and biomass. Soil electrical conductivity (EC), pH, soil texture, soil water content (SWC), soil organic carbon (SOC) and total nitrogen (TN) were determined for each soil sample. Topographic attributes, including elevation and slope, were derived from real-time kinematic (RTK) point elevation data. Interpolated microbial community maps at this scale revealed a spatially structured distribution of microbial biomass and diversity with patches of several hundred meters in different directions corresponding to the distribution of soil types and topography. Most of the microbial communities were autocorrelated at greater ranges within the same soil types than across different soils. The distribution of total soil microbial biomass was mainly affected by SOC and SWC. Soil pH and C:N ratio had a negative impact on the biomass of bacterial communities. Biomass of fungal communities was negatively influenced by slope and elevation. The results of this study have the potential to provide a basis for designing soil sampling plans in characterizing microbial community distribution and site-specific soil health management. [ABSTRACT FROM AUTHOR]

• Geo-tagged tweets were produced as tweet images at 500 m resolution. • Monthly tweet images are closely correlated with VIIRS-DNB images at the pixel level. • Tweet images can be used to assess or map human activities similarly to NTL images. • Monthly tweet images are more stable (free of seasonal effects) than VIIRS-DNB images. With the advent of "social sensing" in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500 m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States. [ABSTRACT FROM AUTHOR]

Permafrost degradation in the Arctic is accelerating and affects northern communities, ecosystems, and global soil carbon storage. However, the extent, distribution, and rates of permafrost degradation in the pan-Arctic remain unknown, contributing to the challenges of mapping and monitoring it in a harsh and remote environment. We applied feature extraction, deep learning, and crowdsourcing to an open-access, high-resolution (2 m), and multi-temporal digital elevation models (i.e. ArcticDEM), to identify retrogressive thaw slumps (RTSs), a dynamic form of permafrost degradation widespread across the Arctic. Specifically, we (1) developed an automated pipeline to process approximately 200 TB of ArcticDEM data; (2) designed feature extractors (pixel-wise elevation differences, polygons of elevation reductions, lines of narrow-steep slopes, and RTS headwall lines) to identify slumps; (3) trained a super-efficient object detection algorithm based on deep learning (YOLOv4) and used it to locate RTSs from composite imagery derived from the ArcticDEM; (4) combined the extracted features and the bounding boxes output by YOLOv4 to obtain mapping results at a manageable level; and (5) developed a crowdsourcing system and invited volunteers to validate and refine the results. The final map included 2494 RTSs (actively expanding during ArcticDEM observation) across the Arctic. The results also show that (1) it is necessary to combine the extracted features and deep learning to remove many false positives in the scenario with limited training data, but large regions to map; (2) some hotspots of RTSs need further and detailed investigation, including an RTS cluster in Greenland; (3) the crowdsourcing system is useful for the validation of a large dataset, but responses to this work were limited, possibly because RTSs are a quite specific topic that not many people are familiar with. The results likely miss many RTSs due to the limitation in the method for identifying RTSs and uncertainties as well as short observation periods of ArcticDEM at many locations. This study provides data and serves as a starting point to develop a global inventory and better understand permafrost thaw in the pan-Arctic using very high resolution remote sensing. • First attempt to map RTSs at a near pan-Arctic scale using high resolution (2 m) data. • Developing an automated pipeline to process approximately 200 TB of data. • Combining feature extraction and deep learning to obtain results at a manageable level. • Developing a crowdsourcing system to bring efforts from the broader community. • Found a cluster of RTSs in Greenland where permafrost research is very limited. [ABSTRACT FROM AUTHOR]

• Proposed the STLCF to modify the unrealistic land cover change. • The STLCF is based on the spatio-temporal transition probability. • The STLCF significantly improved the spatio-temporal consistency of annual land cover maps. Annual analyses of land cover dynamics in urban areas provide a thorough understanding of the urbanization effects on environment and valuable information for the improvement of urban growth modeling. However, most current studies focus on major land cover changes, such as urbanization and vegetation loss. The most feasible way to evaluate the complex interactions among different land cover types is the post-classification change detection, but the temporal inconsistency in the time series of land cover maps impedes the high-frequency and long-term analyses. This study proposed a spatio-temporal land cover filter (STLCF) to remove the illogical land cover change events in the time series of land cover maps, and analyzed the annual land cover dynamics in urban areas. The knowledge of illogical land cover change events was 'learned' from the land cover maps through the spatio-temporal transition probability matrix, instead of experts' knowledge. The illogical change was modified with the land cover of the maximum probability calculated from the naïve Bayesian equation. The STLCF was tested in Wuhan, a typical densely urbanized Chinese city. The annual land cover maps from 2000 to 2013 were derived from multi-date Landsat images using the Decision Tree (DT) classifier. Results showed that the STLCF improved the mean overall accuracy of annual change detection by about 6%. Additionally, the amount of land cover trajectories with unrealistically frequent changes was significantly decreased. During the study period, 7.86% of the pixels experienced one land cover change, and about 0.57% of the pixels experienced land cover changes more than once. The annual analyses demonstrated the non-linear increasing trend in urbanization as well as the corresponding trend in vegetation loss in the study area. We also found the conversion from built-up areas to vegetation near rivers and lakes and in the reserves and rural areas, mainly caused by the restoration of built-up areas to the park or green belt/wedges along rivers and new roads in the metropolitan areas, and to the cropland and woods in the rural areas. Results of this study showed the importance of the spatio-temporal consistency check with knowledge derived from land cover maps of the study area, which facilitates the annual analyses of major and subtle land cover dynamics in urban areas. [ABSTRACT FROM AUTHOR]

A new-generation of night-time lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly composites, have been produced and released by the National Oceanic and Atmospheric Administration's National Centers for Environmental Information. Compared with the last generation NTL image products, the Defense Meteorological Satellite Program's Operational Linescan System stable light composites, the new NTL image products have finer spatial resolution with compatible radiance values across different month/year images. However, the current defects in VIIRS DNB monthly composites show ephemeral lights, relatively high radiance in winter months, and missing data over the high-latitude regions of the northern hemisphere in summer months. This study presents a method to improve the accuracy of the new NTL image products by statistically modelling the time series VIIRS NTL images and uses the improved imagery to estimate socio-economic factors. In this method, we first estimate radiance for each pixel with 'no data' in May, June, July, and August images and then exponentially smooth the monthly time series images to produce a 2014 annual VIIRS DNB image for the contiguous USA. Sum radiance derived from the smoothed annual image shows stronger correlations with gross domestic product at the state level and smaller standard errors of the estimate at the metropolitan and county levels compared with that extracted from the annual image produced by simply averaging the original monthly DNB composites. Such results infer that exponential smoothing effectively improves the quality of the VIIRS DNB images for annual economic estimation. [ABSTRACT FROM AUTHOR]

An accurate mapping of land disturbances with regard to their timing and locations is the prerequisite for the success of downstream disturbance characterization. This study introduces and tests a novel approach that integrates a spatial perspective into the dense Landsat time series analysis, called "Object-Based COntinuous monitoring of Land Disturbance" (OB-COLD). The new algorithm is based on the recognition that the pixels under effects of the same disturbance event often present similar spectral change concurrently in a short time; such pixels experiencing concurrent change could be grouped as an analytic spatial unit, namely "change object". OB-COLD first generates a change-magnitude snapshot every 60 days by applying per-pixel time series analysis to measure spectral change history. Then, an object-based change analysis is applied for each time-stamped snapshot: two levels of change objects are generated through over-segmentation and region merging; the changing area is determined by examining three object-level properties derived at different scales: the average change magnitude, the pre-change cover type, and the object size. Lastly, OB-COLD reconstructs model coefficients for each temporal segment based on the temporal breaks indicated by new snapshot-based change maps. We tested the new algorithm using 3000 randomly selected reference sample plots and eight Landsat ARD tiles across the continental United States. The accuracy assessment suggests that OB-COLD achieved 76.9% producer's accuracy, which is significantly higher than the per-pixel time series algorithm (i.e., COLD) (16.3 percentage increase) while keeping a comparable user's accuracies (58.7% vs. 57.8%). The quantitative and qualitative evaluations both suggest that OB-COLD could significantly reduce omission errors, particularly for stress disturbances. Most commission errors (73%) are attributed to agricultural practices and climate variability. The proposed algorithm is computationally scalable to large-scale spatiotemporal datasets with advanced cyberinfrastructure resources, holding great potential as the base detection algorithm for the next generation of land disturbance products. • OB-COLD integrates spatial information into dense Landsat time series analysis. • Multiscale change objects are generated at a time-slice basis. • Both object-level spatial and spectral properties are considered. • OB-COLD greatly reduces omission errors particularly for stress disturbances. • The algorithm is computationally scalable and efficient. [ABSTRACT FROM AUTHOR]

Public Land Survey ( PLS) data have been widely used in landscape studies of forest and woodlands in the pre- and early- European-settled Midwestern and Western United States. We aim to reconstruct presettlement forest vegetation at a finer spatial resolution than available from the PLS data using environmental covariates (slope, aspect, geology, and soil type) and the spatially correlated structure of witness tree data. To accommodate various data obtained from multiple sources while explicitly taking into account their spatial structures, we adopt a mixed spatially correlated multinomial logit model within the framework of a generalized linear mixed model. The application of the proposed model is illustrated using the three most abundant tree taxa from PLS data in the Arbuckle Mountains of south-central Oklahoma. To assess the influence of each source of information on the spatial prediction, we considered four variant multinomial/spatial models and evaluated their relative predictive power using a validation technique. The probabilistic information about the spatial distribution of tree species obtained from different models reveals the need to integrate information about witness tree data as well as environmental covariates, and the nature of tree species; that is, a tendency to cluster in space to share environmental conditions in the reconstruction of the presettlement forest vegetation surface. Los datos sobre el uso y cobertura de tierras del Public Land Survey (PLS) han sido utilizados ampliamente en estudios de paisaje de bosques y de bosques históricos para periodo previo al asentamiento de migrantes europeos en el medio oeste y oeste de los Estados Unidos. Nuestro objetivo es reconstruir la vegetación forestal previa al asentamiento europeo a una resolución espacial más fina que la disponible actualmente en base a datos del PLS, usando covariables ambientales (pendiente, orientación, geología y tipo de suelo) y la estructura de correlación espacial de los datos de los árboles testigos. Para dar cabida a los diversos datos obtenidos de fuentes múltiples, y a la vez teniendo en cuenta explícitamente sus estructuras espaciales, adoptamos un modelo logit multinomial espacial mixto dentro del marco de los modelos mixtos lineales generalizados (GLMM). La aplicación del modelo propuesto es ilustrada con los tres tipos más abundantes de árboles según los datos del PLS para las montañas de Arbuckle en el centro-sur de Oklahoma, EEUU. Para evaluar la influencia de cada fuente de información sobre la predicción espacial, se consideraron cuatro variantes de los modelos multinomial y espaciales. El poder predictivo de dichos modelos fue evaluado en relación con una técnica de validación. La información probabilística acerca de la distribución espacial de las especies de árboles obtenidos a partir de los diferentes modelos revela que para la reconstrucción de la superficie de la vegetación forestal histórica, es necesario integrar la información sobre los datos de árboles testigos así como las covariables ambientales y la naturaleza de las especies de árboles: es decir, la tendencia de los arboles a agruparse en el espacio para compartir las mismas condiciones ambientales. 公共土地调查(PLS)数据在欧洲人定居美国中西部和西部地区之前以及早期的森林和林地景观研究中得到广泛应用。本文旨在利用环境协变量（坡度、坡向、地貌和土地类型）证据树数据的空间关联结构，重建比PLS数据中更有效的更精细空间分辨率的前殖民期森林植被。为集成多种来源的各类数据，并明确地考虑数据间的空间结构，本文在广义线性混合模型(GLMM)框架下提出了混合空间关联多项Logit模型。以俄克拉荷马州中南部的阿尔布克尔山脉为研究区，提取PLS数据中三种最丰富的树种对模型进行验证。为估计每种信息来源对模型空间预测准确性的影响，本文考虑了4种变异的多项/空间模型并运用验证技术评估它们的相对预测能力。从不同模型获得的树种空间分布的概率信息表明，需要对证据树数据、环境协变量和树种自然属性信息进行集成，也就是说，在重建前殖民期森林植被曲面时，空间上的集聚趋势共享了环境条件。 [ABSTRACT FROM AUTHOR]

With the proposition of the Digital Earth (DE) concept, Virtual Geographic Information System (VGIS) has started to play the role of a Digital Earth prototype system. Many core problems involved in VGIS, such as out-of-core management and interactive rendering of very large scale terrain and image data, have been well studied in the past decades. However, the jitter problem, a common problem in VGIS that often causes annoying visual artefacts and deteriorates the output image quality, draws little attention. In this paper, after an intensive analysis of the jitter problem, a comprehensive framework is proposed to address such a problem while accounting for the characteristics of different data types in VGIS, such as terrain or ocean mesh data, vector data and 3-D model data. Specifically, this framework provides an improved dynamic local coordinate system (DLCS) method for terrain or ocean mesh data. For vector data, the framework provides a simple and effective multiple local coordinate systems (MLCS) method. The framework provides a MLCS method for 3-D model data making full use of the existing local coordinate system of the model. The advantages of the proposed methods over current approaches are analysed and highlighted through case studies involving large GIS datasets. [ABSTRACT FROM AUTHOR]

We develop a multivariate regression model when responses or predictors are on nonlinear manifolds, rather than on Euclidean spaces. The nonlinear constraint makes the problem challenging and needs to be studied carefully. By performing principal component analysis (PCA) on tangent space of manifold, we use principal directions instead in the model. Then, the ordinary regression tools can be utilized. We apply the framework to both shape data (ozone hole contours) and functional data (spectrums of absorbance of meat in Tecator dataset). Specifically, we adopt the square-root velocity function representation and parametrization-invariant metric. Experimental results have shown that we can not only perform powerful regression analysis on the non-Euclidean data but also achieve high prediction accuracy by the constructed model. [ABSTRACT FROM AUTHOR]

Abstract With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM 2.5) and mortality statistics are increasingly used to examine the health effects of PM 2.5. Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM 2.5 -attributable mortality by taking advantage of recent advancements in high resolution mapping of PM 2.5 concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM 2.5 -mortality for the years of 2000, 2005, 2010 and 2015 using three PM 2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM 2.5 -mortality from the 10 km CTM-derived PM 2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM 2.5 datasets. The estimated PM 2.5 -mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM 2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM 2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM 2.5 concentration is the primary factor that leads to the PM 2.5 -attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM 2.5 -attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. Capsule We revisited the estimations of PM 2.5 -attributable mortality with advancements in PM 2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations. Graphical abstract Unlabelled Image Highlights • We revisited estimation of PM 2.5 -attributable mortality using fine-scale of PM 2.5 and mortality datasets • We characterized the impact of geospatial data sources with different geographic characteristics of PM 2.5 -mortlaity study • Mortality estimations using old GEOS-PM 2.5 dataset tend to be smaller than those using new GWR and RFRK-PM 2.5 datasets • PM 2.5 datasets (GWR and RFRK) at the same 1km resolution also lead to different mortality estimations • Effects of mortality statistics on mortality estimations are increasingly larger as zooming into smaller areas [ABSTRACT FROM AUTHOR]

Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors. [ABSTRACT FROM AUTHOR]

Abstract Fluctuations of ambient fine particulate matter (PM 2.5) concentrations show clear yearly and weekly patterns, which has been revealed by previous studies. However, reliability of those studies may be affected by their small research areas, short observation periods, and/or the lack of using specialized statistical approaches for time series. The current study applies a recently developed time-series analysis procedure, Prophet, to investigate seasonality of daily PM 2.5 concentrations over nine years (2007–2015) measured at 220 monitoring stations across the United States. Prophet is a new tool for producing high quality forecasts from time series data that have characteristics of multiple temporal patterns with either linear or non-linear growth/decline. Through decomposing each PM 2.5 time series into three major components (i.e., trend, seasonality, and holidays), we observed periodically changing patterns of PM 2.5 concentrations weekly and yearly consistent with previous findings. Specifically, relatively high PM 2.5 concentrations tend to appear in the month of January and on Fridays, and PM 2.5 concentrations on Sunday are generally lower than those on most other days of the week. However, we discovered that high PM 2.5 concentrations are also likely to appear in July. Additionally, compared to Fridays in most studies, the highest PM 2.5 concentrations are found to more likely occur on Saturdays, while the lowest concentrations are found on Monday as universally as on Sunday. Beyond understanding the seasonality of PM 2.5 concentrations, this study revealed the potential use of Prophet, originally designed for business time series, for detecting periodicities of environmental phenomena. Highlights • Seasonality of PM 2.5 concentrations over the United States was detected. • A new time-series analysis procedure, Prophet, was used to reveal the seasonality. • Yearly highest PM 2.5 is likely to appear in January and July. • Weekly highest PM 2.5 is most likely to appear on Saturday rather than Friday. • Besides Sunday, weekly lowest PM 2.5 is also likely to occur on Monday. [ABSTRACT FROM AUTHOR]

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US usingTwitterdata. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data. [ABSTRACT FROM PUBLISHER]

The first decade of the twenty-first century saw remarkable technological advancements for use in biometeorology. These emerging technologies have allowed for the collection of new data and have further emphasized the need for specific and/or changing systems for efficient data management, data processing, and advanced representations of new data through digital information management systems. This short communication provides an overview of new hardware and software technologies that support biometeorologists in representing and understanding the influence of atmospheric processes on living organisms. [ABSTRACT FROM AUTHOR]

It is known that individual meteorological factors affect the concentrations of fine particulate matter with aerodynamic diameters ≤2.5 μm (PM 2.5 ), yet the specific meteorological effects found in previous studies are largely inconsistent and even conflicting. This study investigates influences of daily and short term changes in synoptic weather on ground-level PM 2.5 concentrations in a large geographical area (75 cities across the contiguous United States (U.S.)) by using ten-year (2001–2010) spatial synoptic classification (SSC) data. We find that in the spring, summer, and fall the presence of the tropical weather types (i.e., dry-tropical (DT) and moist-tropical (MT)) is likely to associate with significantly higher levels of PM 2.5 as compared to an all-weather-type-day average, and the presence of the polar weather types (i.e., dry-polar (DP) and moist-polar (MP)) is associated with significantly lower PM 2.5 concentrations. The short-term (day to day) changes in synoptic weather types in a region are also likely to lead to significant variance in PM 2.5 concentrations. For example, the largest increase in PM 2.5 concentration occurs with the synoptic weather type changing from DP-to-MT. Conversely, a MT-to-DP weather type change results in the largest decrease in PM 2.5 concentrations. Compared to air temperature, the effects of atmospheric moisture on PM 2.5 concentration tend to be subtle, demonstrating that in conjunction with moderate temperature, neither the dry nor the moist air (except moist-moderate (MM) in summer) are associated with significantly high or low PM 2.5 concentrations. Finally, we find that the effects of the synoptic weather type on PM 2.5 concentrations may vary for different seasons and geographical areas. These findings suggest that interactions between atmospheric factors and seasonal and/or geographical factors have considerable impacts on the PM 2.5 concentrations, and therefore should be considered in addition to the SSC when conducting environment health assessments. [ABSTRACT FROM AUTHOR]

Nighttime light imagery is a powerful tool to study urbanization because it can provide a uniform metric, lit area, to delimit urban extents. However, lit area is much larger than actual urban area, so thresholds of digital number (DN) values are usually needed to reduce the lit area. The threshold varies greatly among different regions, but at present it is still not very clear what factors impact the changes of the threshold. In this study, urban extent by province for China is mapped using official statistical data and four intercalibrated and geometrically corrected nighttime light images between 2004 and 2010. Lit area in the imagery for most provinces is at least 94% greater than the official amount of urban area. Regression analyses show a significant correlation between optimal thresholds and GDP per capita, and larger thresholds more commonly indicate higher economic level. Size and environmental condition may explain a province’s threshold that is disproportionate to GDP. Findings indicate one threshold DN is not appropriate for multiple (adjacent) province urban extent mapping, and optimal thresholds for one year may be notably different than the next. Province-level derived thresholds are not appropriate for other geographic levels. Brightness of nighttime lights is an advantage over imagery that relies on daylight reflection, and decreases in brightness indicate faster growth in the horizontal direction than the vertical. A province’s optimal threshold does not always maintain an increase with population and economic growth. In the economically developed eastern provinces, urban population densities decreased (and this is seen in the brightness data), while urban population increased. [ABSTRACT FROM AUTHOR]

The article offers information on Ailments which are related to ambient fine particulate matter (PM2.5) which causes several deaths per year on average worldwide, which, along with high blood pressure and tobacco smoking, is defined as the leading risk factor for global burden of disease. It mentions that the PM2.5 concentrations of the countries with fast economic growth.

Brightness of nighttime lights (NTL) collected by the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) is compatible across different times of images thanks to the on-board calibration system. However, the NTL radiance observed by the S-NPP VIIRS shows clear seasonality corresponding to the seasonal changes in the albedo of land surface. Additionally, the existence of many uncertain factors (e.g. complex atmospheric conditions) renders it inappropriate to directly use the NTL radiances to derive changes on the ground. In this study, we adopt a statistical procedure of time series analysis, namely seasonal and trend decomposition using Loess (STL), to model the time series observations of NTL brightness by decomposing the observations into three separable time series components (i.e. trend, seasonality, and remainder). Based on the time series model, forecast can be made for short-term future with confidence measure, and by comparing the model forecast with observed NTL brightness, significant changes can then be detected at pixel levels. We applied this method to the Puerto Rico area to detect and assess the damages caused by Hurricanes Irma and Maria, and to monitor the recovery after the disaster. Our results show that the proposed method successfully captures the changes of NTL brightness due to the damage of the hurricanes and general economic decline. Moreover, we also find that after removing the seasonal and remainder components, the time series of NTL image can more accurately reflect the temporal trends of economic status in Puerto Rico. • Time-series radiance of NTL from VIIRS-DNB image composites is decomposed by STL. • Seasonal component and noise are removed to detect actual changes in urban areas. • Significant changes in brightness of NTL are detected at pixel levels with confidence levels. • Damages caused by Hurricanes Irma and Maria in Puerto Rico are assessed by our method. [ABSTRACT FROM AUTHOR]

The recent emergence or reemergence of various vector-borne diseases makes the knowledge of disease vectors' presence and distribution of paramount concern for protecting national human and animal health. While several studies have modeled Aedes aegypti or Aedes albopictus distributions in the past five years, studies at a large scale can miss the complexities that contribute to a species' distribution. Many localities in the United States have lacked or had sporadic surveillance conducted for these two species. To address these gaps in the current knowledge of Ae. aegypti and Ae. albopictus distributions in the United States, surveillance was focused on areas in Texas at the margins of their known ranges and in localities that had little or no surveillance conducted in the past. This information was used with a global database of occurrence records to create a predictive model of these two species' distributions in the United States. Additionally, the surveillance data from Texas was used to determine the influence of new data from the margins of a species' known range on predicted species' suitability maps. This information is critical in determining where to focus resources for the future and continued surveillance for these two species of medical concern. [ABSTRACT FROM AUTHOR]

Mass sporting events in the summertime are influenced by underlying weather patterns, with high temperatures posing a risk for spectators and athletes alike. To better understand weather variations in the Greater Toronto Area (GTA) during the Pan American Games in 2015 (PA15 Games), Environment and Climate Change Canada deployed a mesoscale monitoring network system of 53 weather stations. Spatial maps across the GTA demonstrate large variations by heat metric (e.g., maximum temperature, humidex, and wet bulb globe temperature), identifying Hamilton, Ontario as an area of elevated heat and humidity, and hence risk for heat-related illness. A case study of the Hamilton Soccer Center examined on-site thermal comfort during a heat event and PA15 Soccer Games, demonstrating that athletes and spectators were faced with thermal discomfort and a heightened risk of heat-related illness. Results are corroborated by First Aid and emergency response data during the events, as well as insight from personal experiences and Twitter feed. Integrating these results provides new information on potential benefits to society from utilizing mesonet systems during large-scale sporting events. Results further improve our understanding of intra-urban heat variability and heat-health burden. The benefits of utilizing more comprehensive modeling approaches for human heat stress that coincide with fine-scale weather information are discussed. [ABSTRACT FROM AUTHOR]