Geostatistical Analysis Research Articles

Using geostatistical analysis for simultaneous estimation of isoscapes and ontogenetic shifts in isotope ratios of highly migratory marine fish

Tracking migration of highly migratory marine fish using isotope analysis (iso-logging) has become a promising tool in recent years. However, application of this method is often hampered by the lack of essential information such as spatial variations in isotope ratios across habitats (isoscapes) and ontogenetic shifts of isotope ratios of target animals. Here, we test the utility of geostatistical analysis to generate isoscapes of δ13C and δ15N in the western Pacific and estimate the ontogenetic shifts in δ13C and δ15N values of a target species. We first measured δ13C and δ15N in the white muscle of juvenile (n = 210) and adult (n = 884) skipjack tuna Katsuwonus pelamis sampled across the northwest Pacific. Next we fitted a geostatistical model to account for the observed spatial variations in δ13C and δ15N of skipjack by fork length and other environmental variables with spatial random effects. We then used the best-fit models to predict the isoscapes of δ13C and δ15N in 2021. Furthermore, we measured δ15N of amino acids (δ15NAAs) of skipjack (n = 5) to determine whether the observed spatial variation of isotope ratios resulted from baseline shifts or differences in trophic position. The geostatistical model reasonably estimated both isoscapes and ontogenetic shifts from isotope ratios of skipjack, and the isoscapes showed that δ13C and δ15N can clearly distinguish the latitudinal migration of skipjack in the western Pacific. The δ15NAAs supported the results of the geostatistical model, that is, observed variations in skipjack δ15N were largely derived from a baseline shift rather than regional differences in trophic position. Thus, we showed that geostatistical analysis can provide essential basic information required for iso-logging without compound-specific isotope analysis.

Assessment of the spatial variability of selected soil chemical properties using geostatistical analysis in the north-western highlands of Ethiopia

ABSTRACT The aim of this research was to evaluate the status of selected soil chemical properties and to explore their spatial variability in various agroecosystems (AEs) of Choke Mountain, northwestern highlands of Ethiopia. A total of 74 geo-referenced composite surface (0-30 cm) soil samples were collected from representative farm lands. Soil chemical properties were analyzed following standard procedures. In addition, semi-variogram model was applied to estimate the spatial variability of soil properties. The results showed that soil chemical properties are below their threshold values in all agroecosystems and soil groups. The value of soil pH ranged from 4.3 to 6.8; Exchangeable acidity ranged from 0.124 to 0.180 cmol (+) kg−1; EC ranged from 0.02 to 0.19 dS m−1; OC ranged from 0.7 to 3.8%; TN ranged from 0.11 to 0.27%; Exchangeable potassium from 0.29 to 2.0 cmol (+) kg−1and CEC from 22 to 56 cmol (+)kg−1. The results also revealed that soil pH, exchangeable acidity, electrical conductivity, and exchangeable potassium showed strong spatial dependence whereas SOC and TN had moderate spatial dependence. The results indicated that soil quality is deteriorating. Hence, crop, soil and site-specific soil management strategies, primarily focusing on improving the content of SOM and addressing the problems of soil acidity are suggested to improve the soil conditions.

Diagnosis of the Spatial Variability of Soil Nutrients and Economics of Precision Management in Degraded Pastures

Most Brazilian pastures are in some stage of degradation, either by the reduction of soil fertility or inadequate management. The objective of this study was to diagnose the stage of pasture degradation and the diagnosis and management of the spatial variability of available phosphorus (P) and potassium (K+). A total of 100 soil samples were collected at a depth of 0–20 cm for available P and K+ contents determination. Geostatistical analyses of the obtained data were carried out to produce maps of the spatial distribution of available P and K+, using kriging, and allow the recommendation of fertilizers doses. The cost/benefit ratio of fertilizer recommendation and application was evaluated at fixed and variable rates. The different stages of pasture degradation were directly related to the reduction of soil fertility, as well as to the adopted management. The variability of soil available P and K+ presented spatial dependence, and the pattern of distribution resulted in the stage of the pasture degradation. The diagnosis of the level of pasture degradation associated with the use of geostatistical techniques under a precision agriculture perspective favors the efficient use of fertilizers, as well as correct decision-making and cost reduction regarding soil management.

Water conservation capacity under climate and land use change scenarios in Changbai Mountain, China

Abstract Regional water conservation is the key to maintain water supply. Based on the data of 2000, 2010 and 2020, this paper takes Jingyu County in the Changbai Mountain as the study area. By land use conversion matrix, geostatistical analysis, InVEST model and scenario analysis, this paper aims to find out the variation of water conservation and the its related influencing factors in Changbai Mountain region. From 2000 to 2020, forest and cultivated land decreased, while grassland and developed land increased. The water conservation increased from 2.94 × 108 m3 to 4.83 × 108 m3, with a total increase of 64.29%. Forest has the highest average water conservation, followed by grassland, cultivated land and developed land. Climatic factors were important to 91.92% of the study area, whereas 6.22% of it was influenced by land use. When forest decrease in some areas, the main control factor of 48.71% of the area shifted into land use, that could lead to a sharp decrease in water conservation. Climate determines the overall spatial pattern of water conservation; land use determines the local characteristics by controlling runoff. The increase of water conservation is beneficial to improve the reserves and development potential of mineral water.

Assessing the impacts of industrial wastewater on the inland surface water quality: An application of analytic hierarchy process (AHP) model-based water quality index and GIS techniques

The main objective of the present work is to provide a clear insight into receiving inland surface water quality in Kashipur city of Uttarakhand state, India, due to the impact of industrial discharges using a novel Water Quality Index method based on the Analytic Hierarchy Process (AHP) approach. Surface water samples were collected monthly from 12 locations between September 2020 and March 2021. The locations were chosen based on their potential to acquire the most contaminants from surrounding industries and analyzed for their physicochemical characteristics. Geostatistical analysis results represented the spatial variation of the parameter concentrations in the study area. Statistical analyses were performed using Principal Component Analysis and Hierarchical Cluster Analysis. PCA results revealed that two factor loadings impact the surface water chemistry in the study region, where factor 1 represents 50.051% of the variance with six dominant water parameters, and factor 2 represents 33.222% of the total variance. HCA classified the sampling locations randomly into two major clusters. Analytic Hierarchy Process results in establishing the weights of the parameters revealed that dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, and total dissolved solids were the dominant parameters for assessing water quality with a consistency ratio of 0.097. The WQI results showed that 8% of the water samples depict “very poor” inland surface water quality. The anthropogenic sources might be the reason for such poor water quality. This model can be effectively applied at any location and with as many parameters as required to assess surface water quality.

An integration of soil characteristics by using GIS based Geostatistics and multivariate statistics analysis Sultan Batheri block, Wayanad District, India

The study was to estimate the spatial variability of physico-chemical characteristics of soil by using GIS-based geostatistics and multivariate analysis in Sultan Batheri Block, Wayanad district. A combination of geostatistics and multivariate statistical analysis was used to scrutinise and compare the soil data for spatial assessment investigation. In total, 32 soil samples were collected during the summer season (March to May 2021), from different locations through random sampling. Analyzing the characteristics was determined using various standard analytical techniques. The multivariate statistical analysis investigated the excellent results of soil properties. The principal component analysis and correlation coefficient matrix analysis determined the interrelationships of soil properties and showed a good variance of SOC. The highest variation was observed in the SOC whereas the lowest variation was observed in Electrical Conductivity. In PCA analysis, we found that the KMO value is exactly following around 0.788 or slightly below, which is also not exactly optimal. However, since the value is higher than 0.5, soil data factor analysis might be helpful. There are two statistical analyses were used to depict the soil properties and exemplify the spatial variability of soil characteristics in a GIS environment. Inverse distance weighting and kriging were used to develop the spatial distribution of all soil characteristics. According to a prediction generated using ordinary kriging, the SOM shows a high level of spatial autocorrelation in a geostatistical analysis with an elevation range of 2261–2661 m. The R2 coefficient for all soil properties ranges from 0.0072 to 0.2862. The soil variable SOM and the elevation range of 2224 m have a significant spatial autocorrelation in the Kriging semivarigram model. The R2 coefficients for all soil properties ranged from 0.0072 to 0.3162. The interpolation method was relatively accurate for all soil parameters in both types. The cross-validation test results showed that the prediction of soil characteristics was of high accuracy. The levels of nitrogen and phosphorus in the study region are ideal. The study ensured that nitrogen, phosphorus, and other insecticides and herbicides were not used. The result reveals that there is no risk of contamination from nitrogen and phosphorus and that soil is unquestionably contamination-free.

Study on the Impact of Road Traffic Accident Duration Based on Statistical Analysis and Spatial Distribution Characteristics: An Empirical Analysis of Houston

In this study, factors affecting crash duration and geostatistical analysis were examined using traffic crash data from Houston, USA. Significant factors affecting road crash duration included 14 independent factors related to time, roadway, and environment. Delays caused by traffic crashes were used as an indicator to assess the impact of traffic crashes of different severity on the roadway network. In addition, the spatial distribution characteristics of the different values corresponding to each key impact factor under different scenarios in the region were studied using ArcGIS kernel density analysis techniques. This is because the combination of these two methods is more operational and understandable. The results show that accidents are more likely to occur at night and on holidays, that accidents last longer after they occur in residential areas, and that accident duration varies near different road facilities. This study may provide a reference for targeted management and improvement measures for road safety.

Spatial variability of epikarst thickness and its controlling factors in a dolomite catchment

Epikarst, as a distinct layer of karst critical zone, plays a crucial role in water storage-transfer functions and biogeochemical processes. The epikarst thickness, which shows high spatial variability, is an important parameter that controls the dynamic characteristics of the water cycle. Due to the time-cost constraints in conventional field surveys and coarse resolution of remote sensing interpretation, empirical statistical models based on epikarst forming factors are an alternative to quantify the epikarst thickness. In this study, we investigated the spatial variability of epikarst thickness in Huanjiang, Guangxi of southwest China, based on electrical resistivity tomography with 36 sampling lines (1,658 sampling points) in a dolomite catchment. The controlling factors of epikarst thickness were defined based on selecting terrain-vegetation-soil variables with GLM (generalized linear model), RFE (recursive feature elimination) and PLSR (partial least squares regression) methods. Then, the best method for predicting epikarst thickness was selected based on the model with the highest accuracy. Results showed that the mean epikarst thickness was 6.32 m; the epikarst was significantly thicker in the depression (7.2 m) than on the hill slope (5.8 m), whereas there were no significant differences among slope positions. Geostatistical analysis showed that the spherical model reflected the strong spatial autocorrelation of epikarst thickness. Based on the prediction performance of the models, the terrain-vegetation variables alone could not explain epikarst thickness satisfactorily (explanation rate, 32.9 %; testing accuracy, R2 <0.5). By contrast, the modeling accuracy significantly increased (64.7 %, 0.7) when soil properties were considered as predictors. Based on validation criteria (R2 and RMSE), RFE for selecting variables and ANNs for prediction (0.814, 2.137 m) were the optimal model set to predict the epikarst thickness. This study provides a reference method for predicting epikarst thickness at a hillslope scale and the information would be helpful for hydrological catchment modeling.

The heterogeneity of petrophysical and elastic properties in carbonate rocks controlled by strike-slip fault: A case study from yangjikan outcrop in the tarim basin

The petrophysical and elastic properties of the carbonate rocks controlled by strike-slip fault are still poor understood due to their significant complexities and strong heterogeneities, causing challenges for the exploitation and development of these carbonate reservoirs. To better comprehend the heterogeneity of petrophysical and elastic properties of the fault-controlled carbonate rocks, a small carbonate strike slip fault zone is selected, which is located in Yangjikan section in the Tarim basin. We combine the geostatistical, microscopic observation, petrophysical and ultrasonic analyses to characterize and understand the complexities and heterogeneity of petrophysical and elastic properties with different distance to the main fault core. The results show significant complexities and heterogeneities of petrophysical and elastic properties, and the variations of rocks drilled from the fault core are relatively drastic compared to those from the damage zone. The equivalent pore aspect ratios of the rocks are calculated from the differential effective medium theory, the values in the fault core are much higher compared to those from the damage zone, which can discriminate the pore network architectures and can represent reference for determination of the boundary of fractured damage zone. The controlling factors on the heterogeneity of the petrophysical and elastic properties for the selected fault zone are discussed, which mainly include the influence of fault structure position on fracture development, fluid selective filling on effective fracture and pore development, and the sequence on pore development. The results can contribute to the recognition and prediction of the petrophysical and elastic properties and hydrocarbon exploration in carbonate strike-slip fault zone in the Tarim Basin, and can provide reference for the construction of integrate multiscale heterogeneities models.

A k-means-based-approach to analyze the emissions of GHG in the municipalities of MATOPIBA region, Brazil

Brazil is the sixth largest emitter of GHG, with land use change and agriculture being the main source of these emissions. The recent expansion of agriculture in Brazil has been occurring mainly in the MATOPIBA region, a territorial division inserted on Cerrado. Clustering methods are useful for driving hyper-local GHG emission reduction strategies, but they have yet to be applied at the municipal level, nor from the emissions of various economic sectors. In order to contribute to the identification of the most critical areas in relation to GHG emissions for MATOPIBA, this study proposes an approach of municipal clustering according to the percentage contribution of Agriculture, Land Use Change, Energy and Waste sectors in total emissions. The clustering was performed with the k-means algorithm, using the elbow method and the silhouette score to define the number of clusters. In addition, statistical and geostatistical analyses were conducted to assess the consistency and spatial autocorrelation of the groups formed. The approach was able to generate six clusters with distinct characteristics, showing the heterogeneous profile of GHG emissions from MATOPIBA. At the same time, the clustering of similar municipalities can help in making decisions about the best pro-environmental measures to reduce/remove GHG to contain global warming.

Evidence Based Estimation of Macrodispersivity for Groundwater Transport Applications.

The scope of this work is to discuss the proper choice of macrodispersion coefficients in modeling contaminant transport through the advection dispersion equation (ADE). It is common to model solute concentrations in transport by groundwater with the aid of the ADE. Spreading is quantified by macrodispersivity coefficients, which are much larger than the laboratory observed pore-scale dispersivities. In the frame of stochastic theory, longitudinal macrodispersivity is related to the hydraulic conductivity spatial variability via its statistical moments (mean, variance, integral scales), which are generally determined by geostatistical analysis of field measurements. In many cases, especially for preliminary assessment of contaminant spreading, these data are not available and ad hoc values are adopted by practitioners. The present study aims at recommending dispersivity values based on a thorough analysis of tens of field experiments. Aquifers are classified as of weak, medium, and high heterogeneity and for each class a range of macrodispersivity values is recommended. Much less data are available for the transverse macrodispersivities, which are significantly smaller than the longitudinal one. Nevertheless, a few realistic values based on field data, are recommended for applications. Transport models using macrodispersivities can predict mean concentrations, different from the local ones. They can be used for estimation of robust measures, like plumes spatial moments, longitudinal mass distribution and breakthrough curves at control planes.

Earthquake Threats in Ranau – From The Sources of Mensaban and Mesilou Fault

Unlike the majority of Malaysia’s other states, Sabah is characterised by frequent seismological activity; on average, an earthquake of moderate magnitude occurs roughly every 20 years, originating primarily from one of two major sources: either a local source or a regional source. Sabah has seen an upsurge in low to moderate seismic activity in recent years as a result of the fault activities. Between 1900 and lately, magnitudes ranging from MW 2.9 to 6.0 were recorded. While big magnitude earthquakes are relatively uncommon, the area has previously been struck by disastrous earthquakes. Fortunately, the region is demarcated by active Quaternary fault networks. As a result, the area’s seismicity is calculated using line sources corresponding to these faults. Two major fault systems are suspected of being the cause of such activity: the Mensaban fault zone and the Mesilou fault zone, as well as several additional nearby faults. This article explains the process for doing a probabilistic seismic hazard analysis (PSHA) while taking into consideration the peak ground acceleration (PGA) on bedrock in the Ranau region for a 10% and 2% chance of exceedance. The PGA estimate values for Ranau are between 0.08g to 0.16g for PGA 10%, while for PGA 2% was between 0.15g to 0.28g. The PGA value is divided into the following five earthquake hazard classes based on a quarterly geostatistical analysis method. These are “very low,” “low,” “medium,” “high,” and “very high” classes. In general, this quarterly classification is a good way to see how much local seismic activity there is, based on the PGA value. Which can be seen in 10% probability, 19.88 percent of the study area was in “very low,” 20.19 percent was low, 19.98 percent medium, and 19.92 percent high class. 2% probability, it was found that 19.74 percent of the total area was very low class, 20.06 percent low class, 19.79 percent middle class, and 19.82 percent high class. Only 20.6 percent of the area was “very high”.

Leverage and Cook distance in regression with geostatistical data: methodology, simulation, and applications related to geographical information

Regression is often conducted assuming independent model errors. The detection of atypical values in regression (leverage and influential points) assumes independent errors. However, such independence could be unrealistic in geostatistics. In this article, we propose a methodology based on least squares and geostatistics to identify such values in spatial regression. Our procedure uses the hat matrix to detect leverage points. A modified Cook distance is employed to confirm whether these points are influential. The methodology is evaluated with stationary and non-stationary geostatistical data. We apply this methodology to real georeferenced data related to depth, dissolved oxygen, and temperature. First, an autoregressive model is fitted to depth data. Second, a regression between oxygen and temperature is estimated. In both models, spatial correlation is assumed to determine the parameters, leverage, and influential observations. Our methodology can be used in regression with geographical information to avoid misinterpreted results. Not considering this information may under- or over-estimate geographical indicators, such as the mean depth, which can affect the circulation of water masses or dissolved oxygen variability. Our results reveal that including spatial dependence to identify high leverage points is relevant and must be considered in any geostatistical analysis.

Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e, depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder and the bathymetry inversion problem is derived on the low-dimensional latent space in a Hierarchical Bayesian setting. The reformulation allows variational inference with a small number (e.g., O(100) of ROM runs and efficient uncertainty quantification. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.

Population Analysis Using Gis Software Tools – Case Study of Romania

Population management is difficult and complex due to its dynamic and complexity. When we talk about demography, we talk about human population in a defined geographic area, meaning, demography is a spatial science. Geographic Information System (GIS) is a modern tool used in many fields, to manage attribute data and spatial data, including population management. The aim of this study is to present the size of population for each county and urban/rural area on two different years: 2012 and 2020, and the estimation of population growth until the year of 2050. Used for processing spatial data, ArcGIS package from ESRI, allows spatial and geostatistical analysis, mathematical calculations and the visualizations of data. Modern cartography is using GIS technologies to create maps in an easier and flexible way. GIS can use the existing analog maps after digitizing them. Using GIS, specialists can create maps at any scale, centered on any location and showing any selected information. ArcGIS tools permit to visualize, query and overlay the data bases related to the maps. Using ArcGIS applications, we determined the counties which present an increasing of population, and those presenting a decreasing of population, as well as the evolution of population in urban and rural area. In order to determine which counties are affected more by the population decline, we calculated the growth rate using the three major demographic variables: Birth, Death and Migration. In conclusion, we can say that the population in Romania has decreased in the past 8 years and is going to decrease even more in the next 30 years. This happened due to the low birth rate and high emigration rate.

Geostatistical Analysis for Monitoring and Modelling Atmospheric Pollutants

Urbanisation and industrialization are predominant indicators of regional growth with some adverse effect especially in ambient air quality often prone to contamination by emissions. Vehicular emissions have been identified as a consistent air pollutant in urban areas. However, meteorological conditions such as rainfall also affect air pollution concentration level. The aim was to identify relationships between a meteorological factor like rainfall, vehicular load and atmospheric pollutant concentration in Effurun City, Delta State. In-situ sampling of CO, VOC and NO2 and Geostatistical analysis were used to obtain concentration level and relationships between the selected variables which was used to predict spatial trend for efficient monitoring. From our results, it was observed that Iterigbi, Ages Gas and Okuokoko Junctions had the highest concentration of VOC, NO2 and CO respectively. At residential areas, Iterigbi had the highest concentration of VOC and NO2 while Okuokoko had the highest concentration of CO.

Shedding Light on Agritourism in Iberian Cross-Border Regions from a Lodgings Perspective

Agritourism has been widely discussed in the literature. As the range of options for its interpretation increased, studies have come to focus their objectives on its systematization, evaluated with respect to the nature of the interactions between tourists and agriculture, the level of contact with agricultural activities, and the authenticity of the experience. Adding to this complexity is the association of agritourism with experiences related to the agri-food sector, as well as the typologies of accommodation. This research contributes to an improved understanding of agritourism based on evidence from a cross-border region between Portugal and Spain. We identify the links between agriculture and tourism by identifying different categories of agritourism activities via the application of geostatistical analysis. The main results highlight that landscapes, agricultural activities, and agri-food products have a positive influence in creating tourism products and services. Despite this result, passive agritourism activities prevail, such as tasting experiences, contact with endogenous products, enjoyment of the agricultural landscape, or on-farm entertainment. This reality highlights a gap related to authentic agritourism, pointing to the absence of or weak links to agricultural activities and weaknesses in the local “farm-to-table” channel. The grouping analysis results led to the creation of six homogeneous groups, three of which correspond to lodging establishments capable of creating agritourism. However, these groups represent only 39% of the supply. Given this reality, it is of utmost importance to implement policies that encourage tour operators to create agritourism products.

Application of Parallel Factor Analysis (PARAFAC) to the Regional Characterisation of Vineyard Blocks Using Remote Sensing Time Series

Monitoring wine-growing regions and maximising the value of production based on their region/local specificities requires accurate spatial and temporal monitoring. The increasing amount and variability of information from remote sensing data is a potential tool to assess this challenge for the grape and wine industry. This article provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess the value of simultaneously considering spectral and temporal information to highlight site-specific canopy evolution in relation to environmental factors and management practices, which present a large diversity at this regional scale. Parallel Factor Analysis (PARAFAC) was used as an unsupervised technique to recover pure spectra and temporal signatures from multi-way spectral imagery of vineyards in the Languedoc-Roussillon region in the south of France. The model was developed using a time series of Sentinel-2 satellite imagery collected over 4978 vineyard blocks between May 2019 and August 2020. From the Sentinel-2 (spectral and temporal) signal, the PARAFAC analysis allowed the identification of spectral and temporal profiles in the form of pure components, which corresponded to vegetation and soil. The PARAFAC analysis also identified that two of the pure spectra were strongly related to characteristics and dynamics of vineyard cultivation at a regional scale. A conceptual framework was proposed in order to simultaneously consider both vegetation and soil profiles and to summarise the mass of data accordingly. This methodology allowed the computation of a concentration index that characterised how close a field was to a vegetation or a soil profile over the season. The concentration indices were validated for the vegetation and the soil over two growing seasons (2019 and 2020) with geostatistical analysis. A non-random distribution of the concentration index at the regional scale was assumed to highlight a strongly spatially organised phenomenon related to spatially organised environmental factors (soil, climate, training system, etc.). In a second step, spatial patterns of indices were subjected to the expertise of a panel of advisors of the wine industry in order to validate them in relation to vine-growing conditions. Results showed that the introduction of the PARAFAC method opened up the possibility to identify relevant spectro-temporal profiles for vine monitoring purposes.

Multi-phase activity on Ganymede's dark terrain: Tectonic evolution of Galileo Regio

Widespread brittle deformation characterizes the surface of Ganymede. In dark terrain regions, tectonic-related structures occur and represent linear to curvilinear kilometer-scale troughs. Such structures are called furrows and are pivotal to understand the early history of the satellite, the tectonics that affected the dark terrain, and the transition or possible future generation of light terrain. The dark terrain of Galileo Regio shows the highest density of furrows on the satellite at the resolution of Voyager and Galileo imagery. Currently, the origin of such structures is still poorly constrained and strictly related to impact cratering that occurred during Ganymede's geologic history. However, evidence from structural geology interpretation suggests an endogenic origin. In this work, we investigate the structural setting of furrows and deformation structures on Galileo Regio to understand the tectonic regimes responsible for their formation and their relative chronological evolution. Furrows that deform Galileo Regio represent evidence for a multiphase tectonic activity that affected the study area. We perform structural mapping and geostatistical analyses, which allow us to recognize a total of four systems that deform Galileo Regio at both regional- and local-scale. Our analyses allow to understand their azimuthal distribution, deformation intensity and to obtain paleo-stress tensors that show the orientations of the maximum and minimum horizontal stress axes (SHmax/Shmin). The obtained results allow to propose a tectonic model that frames five phases affecting Galileo Regio, which followed a deformation history starting from an impact origin to a tectonic remodeling. Such evolutionary phases refer to a total of three different extensional regimes with Shmin trending i) NW-SE, ii) NE-SW and iii) NNE-SSW, and two strike-slip processes related to iv) NNW-SSE and v) NW-SE trending kinematics. Several phases of the proposed model are consistent with the activity of the adjacent light terrain of Uruk Sulcus and suggest its tectonic control over the dark terrain of Galileo Regio by leading to the formation of two systems. In addition, the last phase of the model shows the current setting on Galileo Regio and suggests possible rejuvenation sites of the dark terrain by the Precursor structures, considered as key elements for the dark/light transition.

Public Transit Equity Analysis at Metropolitan and Local Scales: A Focus on Nine Large Cities in the US

Recent studies on transit service through an equity lens have captured broad trends from the literature and national-level data or analyzed disaggregate data at the local level. This study integrates these methods by employing a geostatistical analysis of new transit access and income data compilations from the Environmental Protection Agency. By using a national data set, this study demonstrates a method for income-based transit equity analysis and provides results spanning nine large auto-oriented cities in the US. Results demonstrate variability among cities’ transit services to low-income populations, with differing results when viewed at the regional and local levels. Regional-level analysis of transit service hides significant variation through spatial averaging, whereas the new data employed in this study demonstrates a block-group scale equity analysis that can be used on a national-scale data set. The methods used can be adapted for evaluation of transit and other modes’ transportation service in areas to evaluate equity at the regional level and at the neighborhood scale while controlling for spatial autocorrelation. Transit service equity planning can be enhanced by employing local Moran’s I to improve local analysis.