Fine-grained inner-annual population data are instrumental in climate change response, resource allocation, and epidemic control. However, such data are currently scarce due to the lack of human-related indicators with both high temporal resolution and long-term coverage that can be used in the process of population spatialization. Here, we estimate monthly 1-km gridded population distribution across China in 2015 using time-series mobile phone positioning data. We construct a hybrid downscaling model to map the gridded population by incorporating random forest and area-to-point kriging. The estimated monthly population products appear to capture inner-annual population variations, especially during special periods, such as the festival, holiday, and short-term labor flow period, which are characterized by large-scale population movements. Additionally, compared with census data, the hybrid model-based results obtained exhibit higher consistency than popular global population products across all spatial extents. Our monthly 1-km data products for the population distribution across China in 2015 provide a credible dataset that can be employed in studies aimed at accurate population-dependent decisions.

Alloy is a lightweight specification language based on relational logic, with an analysis engine that relies on SAT solvers to automate bounded verification of specifications. In spite of its strengths, the reliance of the Alloy Analyzer on computationally heavy solvers means that it can take a significant amount of time to verify software properties, even within limited bounds. This challenge is exacerbated by the ever-evolving nature of complex software systems. This paper presents Platinum , a technique for efficient analysis of evolving Alloy specifications, that recognizes opportunities for constraint reduction and reuse of previously identified constraint solutions. The insight behind Platinum is that formula constraints recur often during the analysis of a single specification and across its revisions, and constraint solutions can be reused over sequences of analyses performed on evolving specifications. Our empirical results show that Platinum substantially reduces (by 66.4% on average) the analysis time required on specifications extracted from real-world software systems.

The modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.

In parallel with the increasing popularity of mobile software, an alarming escalation in the number and sophistication of security threats is observed on mobile platforms, remarkably Android as the dominant platform. Such mobile software, further, evolves incrementally, and especially so when being maintained after it has been deployed. Yet, most security analysis techniques lack the ability to efficiently respond to incremental system changes. Instead, every time the system changes, the entire security analysis has to be repeated from scratch, making it too expensive for practical use, given the frequency with which apps are updated, installed, and removed in such volatile environments as the Android ecosystem. To address this limitation, we present a novel technique, dubbed FLAIR, for efficient, yet formally precise, security analysis of Android apps in response to incremental system changes. Leveraging the fact that the changes are likely to impact only a small fraction of the prior analysis results, FLAIR recomputes the analysis only where required, thereby greatly improving analysis performance without sacrificing the soundness and completeness thereof. Our experimental results using numerous bundles of real-world apps corroborate that FLAIR can provide an order of magnitude speedup over prior techniques.

Formal methods use mathematical notations and logical reasoning to precisely define a program's specifications, from which we can instantiate valid instances of a system. With these techniques we can perform a multitude of tasks to check system dependability. Despite the existence of many automated tools including ones considered lightweight, they still lack a strong adoption in practice. At the crux of this problem, is scalability and applicability to large real world applications. In this paper we show how to relax the completeness guarantee without much loss, since soundness is maintained. We have extended a popular lightweight analysis, Alloy, with a genetic algorithm. Our new tool, EvoAlloy, works at the level of finite relations generated by Kodkod and evolves the chromosomes based on the failed constraints. In a feasibility study we demonstrate that we can find solutions to a set of specifications beyond the scope where traditional Alloy fails. While small specifications take longer with EvoAlloy, the scalability means we can handle larger specifications. Our future vision is that when specifications are small we can maintain both soundness and completeness, but when this fails, EvoAlloy can switch to its genetic algorithm.

Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation-temperature condition index covariates.

Scaling is a fundamental research issue in the geosciences and plays an essential role in the comparison and integration of datasets and in the calibration and validation of environmental models. Much environmental research suffers from a scale discrepancy between different data sources and models[1]. In the validation of remote sensing products, for instance, soil moisture is typically measured in situ at the scale of several dm3, while satellites measure soil moisture for grid cells at least several km2in size[2]. Different spatial scales (supports) in the input and output cause the issue of scale transformation[3]. Various methods have been proposed to transfer the spatial scale of land surface parameters but most are appropriate only for homogeneity situations. In most real-world situations, heterogeneity is inevitably present. This article focuses on scaling methods for land surface parameters under spatial heterogeneity and develops a methodological framework for `scale transformation'. This framework is composed of two types of methods to handling the issue of scale transformation: (1) from multiple in situ observations at point support to obtain satellite footprint-scale estimates (area support), named as MOPTA; (2) from multiple in situ observations at footprint scale (area support) to obtain another footprint-scale estimate (area support), named as MOATA. Land surface parameters considered are soil moisture and evapotranspiration (ET). As to MOPTA, we investigates two cases of upscaling in situ soil-moisture observations to satellite footprint-scale estimates. The in situ observations are acquired by three types of ecohydrological wireless sensor network (WSNs) deployed in 5 cm depth, varying measurement precision. WSNs covers approximately 16 (4 × 4) MODIS 1-km spatial resolution pixels. In the first case, A block kriging (BK) upscaling strategy is used to scale up soil moisture to MODIS pixel averages with in situ observations of unequal precision[4]. Furthermore, when measurement times of ground-based and satellite-based observations are not the same, temporal variation in soil moisture must be taken into account. At this case, a spatio-temporal regression block kriging (STRBK) is used to upscale in situ soil moisture observations collected as time series at multiple locations to pixel-scale estimates for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe watershed[5]. As to MOATA, two types of in situ observations are involved: eddy correlation (EC) which measurements are normally a few to hundreds of meters[6][7] and large aperture scintillometer (LAS) which measurements are integrated over a long transect of approximately 500-5000 m from the same or different underlying surfaces[6][7]. For the purpose of cross-validation and comparison, the scale transformation between EC observations and LAS observations needs to be carried out. A area-to-area regression kriging is used to handle the problem of the nonstationarity of a random function and the issue of scale transformation[8]. This framework will be further developed to handling more cases. When land surface parameter show high spatio(-temporal) heterogeneity within the footprint, the upscaling strategy will take this into account through modelling the mean and variance as non-constant values that depend on high-resolution covariates. We will do this both in the spatial and spatio-temporal setting, in the case of the latter making use of space-time geostatistics and the stochastic partial differential equation (PDE) approach.

Surface sensible heat flux (SHF) is a critical indicator for understanding heat exchange at the land-atmosphere interface. A common method for estimating regional SHF is to use ground observations with approaches such as eddy correlation (EC) or the use of a large aperture scintillometer (LAS). However, data observed by these different methods might have an issue with different spatial supports for cross-validation and comparison. This letter utilizes a geostatistical method called area-to-area regression kriging (ATARK) to solve this problem. The approach is illustrated by upscaling SHF from EC to LAS supports in the Heihe River basin, China. To construct a point support variogram, a likelihood function of four parameters (nugget, sill, range, and shape parameters) conditioned by EC observations is used. The results testify to the applicability of ATARK as a solution for upscaling SHF from EC support to LAS support.

This paper introduces a weighted total least squares (WTLS)-based estimator into image registration to deal with the coordinates of control points (CPs) that are of unequal accuracy. The performance of the estimator is investigated by means of simulation experiments using different coordinate errors. Comparisons with ordinary least squares (LS), total LS (TLS), scaled TLS, and weighted LS estimators are made. A novel adaptive weight determination scheme is applied to experiments with remotely sensed images. These illustrate the practicability and effectiveness of the proposed registration method by collecting CPs with different-sized errors from multiple reference images with different spatial resolutions. This paper concludes that the WTLS-based iteratively reweighted TLS method achieves a more robust estimation of model parameters and higher registration accuracy if heteroscedastic errors occur in both the coordinates of reference CPs and target CPs.

The estimation of regional gross primary production (GPP) is a crucial issue in carbon cycle studies. One commonly used way to estimate the characteristics of GPP is to infer the total amount of GPP by collecting field samples. In this process, the spatial sampling design will affect the error variance of GPP estimation. This letter uses geostatistical model-based sampling to optimize the sampling locations in a spatial heterogeneous area. The approach is illustrated with a real-world application of designing a sampling strategy for estimating the regional GPP in the Babao river basin, China. By considering the heterogeneities in the spatial distribution of the GPP, the sampling locations were optimized by minimizing the spatially averaged interpolation error variance. To accelerate the optimization process, a spatial simulated annealing search algorithm was employed. Compared with a sampling design without considering stratification and anisotropies, the proposed sampling method reduced the error variance of regional GPP estimation.

Upscaling ground-based moisture observations to satellite footprint-scale estimates is an important problem in remote sensing soil-moisture product validation. The reliability of validation is sensitive to the quality of input observation data and the upscaling strategy. This letter proposes a model-based geostatistical approach to scale up soil moisture with observations of unequal precision. It incorporates unequal precision in the spatial covariance structure and uses Monte Carlo simulation in combination with a block kriging (BK) upscaling strategy. The approach is illustrated with a real-world application for upscaling soil moisture in the Heihe Watershed Allied Telemetry Experimental Research experiment. The results show that BK with unequal precision observations can consider both random ground-based measurement errors and upscaling model error to achieve more reliable estimates. We conclude that this approach is appropriate to quantify upscaling uncertainties and to investigate the error propagation process in soil-moisture upscaling.