Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Dimensional dependence of the Stokes-Einstein relation and its violation

We generalize to higher spatial dimensions the Stokes-Einstein relation (SER) as well as the leading correction to diffusivity in finite systems with periodic boundary conditions, and validate these results with numerical simulations. We then investigate the evolution of the high-density SER violation with dimension in simple hard sphere glass formers. The analysis suggests that this SER violation disappears around dimension du = 8, above which it is not observed. The critical exponent associated with the violation appears to evolve linearly in 8 - d, below d = 8, as predicted by Biroli and Bouchaud [J. Phys.: Condens. Matter 19, 205101 (2007)], but the linear coefficient is not consistent with the prediction. The SER violation with d establishes a new benchmark for theory, and its complete description remains an open problem. © 2013 AIP Publishing LLC.

A Spatial Analysis to Permanent Income as Deterrent of Homicides: the case of Medellin City

This paper studies the relationship between permanent income and homicides, estimating an income-crime elasticity. We assume that this elasticity varies across geographical areas. We estimate different specifications of Spatial Panel Models using information of urban areas in Medellin (Colombia), areas known as communes. Spatial Models consider the importance of location and the type of neighbors of each commune. We simulate an intervention over permanent income in order to estimate the income elasticity for each commune and the average elasticity of income-crime on the city. We provide evidence about spatial dependence between the homicides per commune and their neighbors, and about a relationship between homicides and neighbor’s income. In our case of study, the average estimated impact of 1% increase in permanent income in a specific commune produces a decrease in the homicide rate on average in 0.39%. Finally, permanent income plays a crime deterrent role, but also this effect of income on crime varies across the city, showing that some areas are strategically located to this kind of intervention.

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Assessment of the spatial variability in Tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones

Little information is available on the degree of within-field variability of potential production of Tall wheatgrass (Thinopyrum ponticum) forage under unirrigated conditions. The aim of this study was to characterize the spatial variability of the accumulated biomass (AB) without nutritional limitations through vegetation indexes, and then use this information to determine potential management zones. A 27-×-27-m grid cell size was chosen and 84 biomass sampling areas (BSA), each 2 m(2) in size, were georeferenced. Nitrogen and phosphorus fertilizers were applied after an initial cut at 3 cm height. At 500 °C day, the AB from each sampling area, was collected and evaluated. The spatial variability of AB was estimated more accurately using the Normalized Difference Vegetation Index (NDVI), calculated from LANDSAT 8 images obtained on 24 November 2014 (NDVInov) and 10 December 2014 (NDVIdec) because the potential AB was highly associated with NDVInov and NDVIdec (r (2) = 0.85 and 0.83, respectively). These models between the potential AB data and NDVI were evaluated by root mean squared error (RMSE) and relative root mean squared error (RRMSE). This last coefficient was 12 and 15 % for NDVInov and NDVIdec, respectively. Potential AB and NDVI spatial correlation were quantified with semivariograms. The spatial dependence of AB was low. Six classes of NDVI were analyzed for comparison, and two management zones (MZ) were established with them. In order to evaluate if the NDVI method allows us to delimit MZ with different attainable yields, the AB estimated for these MZ were compared through an ANOVA test. The potential AB had significant differences among MZ. Based on these findings, it can be concluded that NDVI obtained from LANDSAT 8 images can be reliably used for creating MZ in soils under permanent pastures dominated by Tall wheatgrass.

Spatial variability of satured soil hydraulic conductivity in the region of Araguaia River - Brazil.

This study evaluates the spatial variability of saturated hydraulic conductivity in the soil in an area of 51,850 ha at the headwaters of the Araguaia River MT/GO. This area is highly vulnerable because it is a location of recharging through natural water infiltration of the Guarani Aquifer System and an area of intense increases in agriculture since its adoption by growers in the last 30 years. Soil samples were collected at 383 points, geographically located by GPS. The samples were collected from depths of 0 - 20 cm and 60 - 80 cm. Exploratory statistics and box-plot were used in the descriptive analysis and semivariogram were constructed to determine the spatial model. The exploratory analysis showed that the mean hydraulic conductivity in the superficial layer was less than at the level of 60-80 cm; however, the greatest variability evaluated with a coefficient of variation also was from this layer. Data tended towards a normal distribution. These results can be explained by the greater soil compaction in the superficial layer. The semivariogram models, adjusted for the two layers, were exponential and demonstrated moderate and strong dependence, with ranges of 5000 and 3000 utm respectively. It was concluded that soil use is influencing the spatial distribution model of the hydraulic conductivity in the region.

Modelling spillover effects in spatial stochastic frontier analysis

In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

Beam-energy Dependence Of The Directed Flow Of Protons, Antiprotons, And Pions In Au+au Collisions.

Rapidity-odd directed flow (v1) measurements for charged pions, protons, and antiprotons near midrapidity (y=0) are reported in sNN=7.7, 11.5, 19.6, 27, 39, 62.4, and 200 GeV Au+Au collisions as recorded by the STAR detector at the Relativistic Heavy Ion Collider. At intermediate impact parameters, the proton and net-proton slope parameter dv1/dy|y=0 shows a minimum between 11.5 and 19.6 GeV. In addition, the net-proton dv1/dy|y=0 changes sign twice between 7.7 and 39 GeV. The proton and net-proton results qualitatively resemble predictions of a hydrodynamic model with a first-order phase transition from hadronic matter to deconfined matter, and differ from hadronic transport calculations.

Beam-energy Dependence Of Charge Separation Along The Magnetic Field In Au+au Collisions At Rhic.

Local parity-odd domains are theorized to form inside a quark-gluon plasma which has been produced in high-energy heavy-ion collisions. The local parity-odd domains manifest themselves as charge separation along the magnetic field axis via the chiral magnetic effect. The experimental observation of charge separation has previously been reported for heavy-ion collisions at the top RHIC energies. In this Letter, we present the results of the beam-energy dependence of the charge correlations in Au+Au collisions at midrapidity for center-of-mass energies of 7.7, 11.5, 19.6, 27, 39, and 62.4 GeV from the STAR experiment. After background subtraction, the signal gradually reduces with decreased beam energy and tends to vanish by 7.7 GeV. This implies the dominance of hadronic interactions over partonic ones at lower collision energies.

Beam Energy Dependence Of Moments Of The Net-charge Multiplicity Distributions In Au+au Collisions At Rhic.

We report the first measurements of the moments--mean (M), variance (σ(2)), skewness (S), and kurtosis (κ)--of the net-charge multiplicity distributions at midrapidity in Au+Au collisions at seven energies, ranging from sqrt[sNN]=7.7 to 200 GeV, as a part of the Beam Energy Scan program at RHIC. The moments are related to the thermodynamic susceptibilities of net charge, and are sensitive to the location of the QCD critical point. We compare the products of the moments, σ(2)/M, Sσ, and κσ(2), with the expectations from Poisson and negative binomial distributions (NBDs). The Sσ values deviate from the Poisson baseline and are close to the NBD baseline, while the κσ(2) values tend to lie between the two. Within the present uncertainties, our data do not show nonmonotonic behavior as a function of collision energy. These measurements provide a valuable tool to extract the freeze-out parameters in heavy-ion collisions by comparing with theoretical models.

Non-gaussian Spatial Correlations Dramatically Weaken Localization.

We perform variational studies of the interaction-localization problem to describe the interaction-induced renormalizations of the effective (screened) random potential seen by quasiparticles. Here we present results of careful finite-size scaling studies for the conductance of disordered Hubbard chains at half-filling and zero temperature. While our results indicate that quasiparticle wave functions remain exponentially localized even in the presence of moderate to strong repulsive interactions, we show that interactions produce a strong decrease of the characteristic conductance scale g^{*} signaling the crossover to strong localization. This effect, which cannot be captured by a simple renormalization of the disorder strength, instead reflects a peculiar non-Gaussian form of the spatial correlations of the screened disordered potential, a hitherto neglected mechanism to dramatically reduce the impact of Anderson localization (interference) effects.

Amostragem e variabilidade espacial de atributos químicos do solo em área de vegetação natural em regeneração

The objectives of this work was to estimate the number of soil subsamples considering the classical statistics and geostatistics and determine the spatial variability of soil fertility attributes of an Ultisol, with clay texture, in an area of regenerating natural vegetation in Alegre - ES. Soil samples were collected in a depth of 0.0-0.2 m, at the crossing points of a regular grid, comprising a total of 64 points located at 10 m-intervals. The area presented low fertility soil. Considering a variation of 5% around the mean in the classic statistics, it is necessary a larger number of samples in relation to geostatistics. All the chemical attributes showed moderate to high spatial dependence, except for the effective cation exchange capacity (CECe), which showed pure nugget effect. The spherical semivariogram model gave the best fit to the data. Isoline maps allowed visualizing the differentiated spatial distribution of the contents of soil chemical attributes.

Variabilidade espacial de atributos físicos de um argissolo vermelho-amarelo sob pastagem e vegetação secundária em regeneração natural

This work was carried out with the objective of studying the spatial variability of the physical attributes of a Red-Yellow Ultisol under pasture and secondary vegetation in natural regeneration. Two areas were chosen in a hillside, with the soil sampling to the depth of 0-0.2 m, with the georeferenced points in a regular grid of 10x10 m, totalizing 64 points. In each point it was evaluated the total volume of porosity, macroporosity, microporosity, bulk density, soil penetration resistance and soil water content. The studied attributes in the pasture area present indicator of soil compaction for the animals' traffic, with moderate and strong structure of spatial dependence, except for the macroporosity and penetration resistance. In the area of secondary vegetation (VN) only the macroporosity does not present spatial dependence. The total volume of porosity and the bulk density present the same spatial standard in the area under pasture.

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Onion (Allium cepa) is one of the most cultivated and consumed vegetables in Brazil and its importance is due to the large laborforce involved. One of the main pests that affect this crop is the Onion Thrips (Thrips tabaci), but the spatial distribution of this insect, although important, has not been considered in crop management recommendations, experimental planning or sampling procedures. Our purpose here is to consider statistical tools to detect and model spatial patterns of the occurrence of the onion thrips. In order to characterize the spatial distribution pattern of the Onion Thrips a survey was carried out to record the number of insects in each development phase on onion plant leaves, on different dates and sample locations, in four rural properties with neighboring farms under different infestation levels and planting methods. The Mantel randomization test proved to be a useful tool to test for spatial correlation which, when detected, was described by a mixed spatial Poisson model with a geostatistical random component and parameters allowing for a characterization of the spatial pattern, as well as the production of prediction maps of susceptibility to levels of infestation throughout the area.