Spatial Interpolation Research Articles

J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models

Abstract. New-particle formation from condensable gases is a common atmospheric process that has significant but uncertain effects on aerosol particle number concentrations and aerosol–cloud–climate interactions. Assessing the formation rates of nanometer-sized particles from different vapors is an active field of research within atmospheric sciences, with new data being constantly produced by molecular modeling and experimental studies. Such data can be used in large-scale climate and air quality models through parameterizations or lookup tables. Molecular cluster dynamics modeling, ideally benchmarked against measurements when available for the given precursor vapors, provides a straightforward means to calculate high-resolution formation rate data over wide ranges of atmospheric conditions. Ideally, the incorporation of such data should be easy, efficient and flexible in the sense that same tools can be conveniently applied for different data sets in which the formation rate depends on different parameters. In this work, we present a tool to generate and interpolate lookup tables of formation rates for user-defined input parameters. The table generator primarily applies cluster dynamics modeling to calculate formation rates from an input quantum chemistry data set defined by the user, but the interpolator may also be used for tables generated by other models or data sources. The interpolation routine uses a multivariate interpolation algorithm, which is applicable to different numbers of independent parameters, and gives fast and accurate results with typical interpolation errors of up to a few percent. These routines facilitate the implementation and testing of different aerosol formation rate predictions in large-scale models, allowing the straightforward inclusion of new or updated data without the need to apply separate parameterizations or routines for different data sets that involve different chemical compounds or other parameters.

CMADS and CFSR Data-Driven SWAT Modeling for Impacts of Climate and Land-Use Change on Runoff

Climate and land-use change significantly impact hydrological processes and water resources management. However, studies of runoff simulation accuracy and attribution analysis in large-scale basins based on multi-source data and different scenario projections are limited. This study employed the Soil and Water Assessment Tool (SWAT) model in conjunction with spatial interpolation techniques to evaluate the accuracy of Climate Forecast System Reanalysis (CFSR), China Meteorological Assimilation Driven Dataset (CMADS), and observation (OBS) in runoff simulations, and configured various scenarios using the Patch-generating Land-use Simulation (PLUS) model to analyze effects of climate and land-use changes on runoff in the Jing River Basin from 1999 to 2018. Results demonstrated the superior performance of the CMADS+SWAT model compared to than CFSR+SWAT model, as the latter underestimated peak runoff. Changes in precipitation had a stronger impact on runoff than temperature, with increased flow from farmland and strong interception effects from forestland. Integrated climate and land-use changes led to an average annual runoff reduction of 1.24 m3/s (I2), primarily attributed to climate change (1.12 m3/s, I3), with a small contribution from land-use change (0.12 m3/s, I4). CMADS exhibited robust applicability under diverse scenarios, effectively enhancing runoff simulation accuracy. The findings provide invaluable guidance for water resources management in semi-arid regions.

Bioremediation of Oil Contaminated Soil and Restoration of Land Historically Polluted with Oil Products in the Agricultural Circuit in the Plain and Western Hills, Romania

Oil contamination in soil from a variety of sources, including accidental leaks, industrial activities, and improper waste disposal, involves disrupting ecosystems, contaminating water, harming human health, and diminishing agricultural productivity. Bioremediation is becoming the most important method accepted as a treatment for hydrocarbon-polluted soil using indigenous microbial flora, which aims to restore soils to their pre-hydrocarbon pollution characteristics. We will follow in our article to give some examples of good practices for bioremediation of oil-polluted soils in some sites in NE Romania. In a first step, all the oil extraction wells in NW Romania were mapped, both those in operation and those abandoned, reaching 695 wells. For 7 case studies, soil profiles were taken from the vicinity of the well column and from points close to it located in the well casing, for which the concentration of total petroleum hydrocarbons was determined. Using GIS spatial interpolation techniques, the theoretical concentration of petroleum hydrocarbons in the soil was determined. The polluted soil was transported to the bioremediation station, where it was exposed to bioremediation procedures, and the period and duration until the soil was brought to the accepted parameters in terms of allowable concentrations were analysed. The time required varies between 17 and 36 weeks needed for bioremediation. Following practical applications, it can be concluded that the time required for bioremediation is directly dependent on the initial concentration of pollutants and the number of chemical and physical interventions applied to the soil.

Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016

Satellite aerosol products are pivotal in studies of regional air quality and global climate change. Compared with accurate in situ observations, satellite measurements provide valuable large-scale atmospheric information. However, limitations such as clouds and retrieval assumptions result in a significant number of missing values in satellite aerosol optical depth (AOD) products, which severely hampers the representativeness. To address this issue, spatial interpolation of the AOD data is necessary to improve data coverage. In this study, one year of AOD observation data from the MODIS C6.1 version was applied to analyze the spatiotemporal correlated characteristics. The statistical parameters were used as dynamic interpolation weights to develop a novel interpolation method called empirical correlation weighting (ECW) based on MODIS AOD over Northern China in 2016. The ECW interpolation results were obtained at a 0.05° resolution (~5 km). The results showed that the spatial coverage of the Deep Blue (DB) and Dark Target (DT) products increased from 43.88% to 70.65% and from 15.04% to 32.62%, respectively. The reconstruction of the ECW method illustrated good agreement with original values in three cases and in two experimental areas. The mean absolute error (MAE) and root mean square error (RMSE) in the two experiments were 0.1171 and 0.0809, and 0.1212 and 0.0838, respectively, indicating that the ECW exhibited the better accuracy than ordinary Kriging (OK) and Thin Plate Spline (TPS). The AERONET validation results indicated that the values of RMSE and MAE were slightly higher after interpolation compared with those before interpolation, maintaining relatively low values, 0.241 and 0.257, 0.140 and 0.150, respectively.

Estimation of Hypoxic Areas in the Western Baltic Sea with Geostatistical Models

Dissolved oxygen is essential for all marine life, especially for benthic organisms that live on the seafloor and are unable to escape if oxygen concentrations fall below critical thresholds. Therefore, near-bottom oxygen concentrations are a key component of environmental assessments and are measured widely. To gain the full picture of hypoxic areas, spatial gaps between monitoring stations must be closed. Therefore, we applied two spatial interpolation methods, where estimated near-bottom oxygen concentrations were solely based on measurements. Furthermore, two variants of the machine learning algorithm Quantile Regression Forest were applied, and any uncertainties in the results were evaluated. All geostatistical methods were evaluated for one year and over a longer period, showing that Quantile Regression Forest methods achieved better results for both. Afterward, all geostatistical methods were applied to estimate the areas below different critical oxygen thresholds from 1950 to 2019 to compute oxygen-deficient areas and how they changed when faced with anthropogenic pressures, especially in terms of increased nutrient inputs.

Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

A Hybrid Similarity-Based Method for Wind Monitoring System Deployment Optimization Along Urban Railways

Urban railways in coastal areas are exposed to the risk of extreme weather conditions. A cost-effective and robust wind monitoring system, as a vital part of the railway infrastructure, is essential for ensuring safety and efficiency. However, insufficient sensors along urban rail lines may result in failure to detect local strong winds, thus impacting urban rail safety and operational efficiency. This paper proposes a hybrid method based on historical wind speed data analysis to optimize wind monitoring system deployment. The proposed methodology integrates warning similarity and trend similarity with a linear combination and develops a constrained quadratic programming model to determine the combined weights. The methodology is demonstrated and verified based on a real-world case of an urban rail line. The results show that the proposed method outperforms the single similarity-based method and spatial interpolation approach in terms of both evaluation accuracy and robustness. This study provides a practical data-driven tool for urban rail operators to optimize their wind sensor networks with limited data and resources. It can contribute significantly to enhancing railway system operational efficiency and reducing the hazards on rail infrastructures and facilities under strong wind conditions. Additionally, the novel methodology and evaluation framework can be efficiently applied to the monitoring of other extreme weather conditions, further enhancing urban rail safety.

Spatial correlation models of VS30 values: A case study of the Tehran region

The average shear wave velocity of the top 30 m of soil profile (VS30) is considered as an indicator of local-site effects in seismic hazard assessment (SHA) studies. Previous research works have demonstrated that in addition to actual VS30 values, their spatial correlation characteristics have considerable effects on the estimated values of earthquake intensity measurements, particularly when the seismic risk assessment of spatially distributed assets and infrastructure networks is of interest. Tehran city, in addition to its proximity to several faults, is situated on a variety of sedimentary textures and encompasses a large number of residential buildings and infrastructure networks. The current study utilizes advanced spatial statistics methods and investigates the spatial correlation characteristics of the random field of VS30 values in Tehran. Moreover, by employing a more comprehensive database of VS30 values measured using geoseismic approaches and using eight spatial interpolation methods, a new estimated VS30 map of Tehran city is provided. The geological formations of the study area are also investigated considering the distribution of sample points with different VS30 values. The findings of the current research can be utilized in spatially correlated SHA studies in the Tehran region and can be viewed as a step towards filling the data gap required for seismic risk assessment studies in this region. Moreover, by identifying the local variations in site classes, the findings of the current study can be used for further detailing of geological formations.

The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning

Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction.

Estimating daily air temperature and pollution in Catalonia: A comprehensive spatiotemporal modelling of multiple exposures

Environmental epidemiology studies require models of multiple exposures to adjust for co-exposure and explore interactions. We estimated spatiotemporal exposure to surface air temperature and pollution (PM2.5, PM10, NO2, O3) at high spatiotemporal resolution (daily, 250 m) for 2018–2020 in Catalonia. Innovations include the use of TROPOMI products, a data split for remote sensing gap-filling evaluation, estimation of prediction uncertainty, and use of explainable machine learning.We compiled meteorological and air quality station measurements, climate and atmospheric composition reanalyses, remote sensing products, and other spatiotemporal data. We performed gap-filling of remotely-sensed products using Random Forest (RF) models and validated them using Out-Of-Bag (OOB) samples and a structured data split. The exposure modelling workflow consisted of: 1) PM2.5 station imputation with PM10 data; 2) quantile RF (QRF) model fitting; and 3) geostatistical residual spatial interpolation. Prediction uncertainty was estimated using QRF. SHAP values were used to examine variable importance and the fitted relationships. Model performance was assessed via nested CV at the station level.Evaluation of the gap-filling models using the structured split showed error underestimation when using OOB. Temperature models had the best performance (R2 =0.98) followed by the gaseous air pollutants (R2 =0.81 for NO2 and 0.86 for O3), while the performance of the PM2.5 and PM10 models was lower (R2 =0.57 and 0.63 respectively). Predicted exposure patterns captured urban heat island effects, dust advection events, and NO2 hotspots. SHAP values estimated a high importance of TROPOMI tropospheric NO2 columns in PM and NO2 models, and confirmed that the fitted associations conformed to prior knowledge.Our work highlights the importance of correctly validating gap-filling models and the potential of TROPOMI measurements. Moderate performance in PM models can be partly explained by the poor station coverage. Our exposure estimates can be used in epidemiological studies potentially accounting for exposure uncertainty.

Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver model

Abstract Visualisation of road traffic noise is vital for traffic noise planning policies. Several factors affect the noise from road traffic with physical and environmental conditions. Collecting noise levels around the world is not a possible task. Therefore, calculating noise levels by a valid noise model, and spatial interpolations, is prime to traffic noise visualisation. In this study, the Henk de Klujijver noise model is used. Designing noise observation points (Nops) embedding with a three-dimensional (3D) building model and identifying the best suitable spatial interpolation are important to visualise the traffic noise accurately. However, interpolating noise in 3D space (vertical direction) is a more complex process than interpolating in two-dimensional (2D) space. Flat triangles should be eliminated in the vertical direction. Therefore, the structure of Nop has a major influence on spatial interpolation. Triangular Irregular Network (TIN) interpolation is more accurate for visualising traffic noise as 3D noise contours than Inverse Distance Weighted and kriging. Although kriging is vital to visualise noise as raster formats in 2D space. The 3D kriging in Empirical Bayesian shows a 3D voxel visualisation with higher accuracy than 3D TIN noise contours.

LADI: Landslide displacement interpolation through a spatial-temporal Kalman filter

Monitoring landslide displacement is critical towards understanding kinematics and evaluating risk. Both remote sensing technology and in-situ sensors are currently used for monitoring purposes, each with their own spatio-temporal advantages. However, the majority of current workflows analyze these data independently, missing a synergistic opportunity to combine complementary datasets to enhance analyses both spatially and temporally. This work presents the first landslide specific displacement interpolation technique using a novel approach, LADI (Landslide Displacement Interpolation), which creates a high-spatial, high-temporal resolution interpolation of landslide surface displacement by combining remotely-sensed observation data with and in-situ sensor data. At a minimum, LADI requires only a single in-situ monitoring station (control point) and a single high-resolution displacement dataset (calibration data) to function. LADI implements a series of Kalman filters, where interpolation is first performed spatially, then optionally in the temporal domain. LADI is demonstrated on both simulated and real-world landslide data to showcase the behavior, effectiveness, and applicability of the method. Results are compared to common spatial interpolation methods, which demonstrate that LADI achieves superior performance (average EPE = 0.018 m and 0.031 m of LADI and the best performing common spatial interpolation method respectively when number of interpolation control points = 4) in the real-world test sparse displacement dataset when the number of spatial control points is less than sixteen. Additionally, the performance gain of utilizing LADI is further enhanced over common interpolation methods when the interpolation control points are not equally distributed geographically (average EPE = 0.016 m and 0.049 m respectively) as is usually the case with in-situ landslide displacement monitoring stations.

Prediction and model evaluation for space–time data

ABSTRACT Evaluation metrics for prediction error, model selection and model averaging on space–time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space–time prediction problems. Motivated by air pollution data collected during California wildfires in 2008, this manuscript attempts a formalization of the true prediction error associated with spatial interpolation. We investigate a variety of cross-validation (CV) procedures employing both simulations and case studies to provide insight into the nature of the estimand targeted by alternative data partition strategies. Consistent with recent best practice, we find that location-based cross-validation is appropriate for estimating spatial interpolation error as in our analysis of the California wildfire data. Interestingly, commonly held notions of bias-variance trade-off of CV fold size do not trivially apply to dependent data, and we recommend leave-one-location-out (LOLO) CV as the preferred prediction error metric for spatial interpolation.

On spatial interpolation of ocean energy source variables: A comparative analysis

In the context of wave energy systems development, the estimation of wave parameters such as significant wave height (Hs) and energy period (Te) over the entire ocean surface is of paramount importance. These information are crucial for estimating the energy harvesting potential of deployment sites, designing wave energy converters (WECs), planning optimal maintenance intervention frequency, and assessing the impact of waves on coastal communities. However, measuring Hs and the Te at every point in the ocean is impossible due to the vastness of the ocean and due to the cost and difficulties of installing and maintaining wave instrumentation buoys, since these have to survive in marine environment, which is particularly hostile. As a consequence, the amount of data is too limited and sparse in space for the practical and precise performance of these analyses. To address such data scarcity and sparsity, we analyse in this paper various spatial interpolation techniques employed to fill the spatial gaps in the wave parameter datasets. These techniques are compared in their performances in estimating the wave parameters in space given a set of known measurements in limited sampling locations. Three types of interpolators are considered: linear interpolator, spline interpolator, and radial basis functions (RBFs) interpolator. These algorithms are trained and tested on a public dataset of wave parameters from Copernicus Marine Service in an area between the coastlines of South England and North France. To simulate the available data scarcity and sparsity, only limited percentages of the ocean area are considered covered and available in the training stage (from 0.01% to 1%). The performance of each interpolator is evaluated on the remainder of the considered area in terms of Normalized Root Mean Square Error (NRMSE) and on the Normalized Mean Absolute Error (NMAE) achieved by the algorithm in reconstructing the parameters at these unsampled locations. The results of this study demonstrate the feasibility of spatial gap-filling of wave parameters, and demonstrates that the RBF algorithm outperforms the other two algorithms both in performances and, even more, in terms of robustness to different training points sampling.

Improvement of methods of obtaining urban TMY and application for building energy consumption simulation

In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs.

A Hydrogeological Conceptual Model Refines the Behavior of a Mediterranean Coastal Aquifer System: A Key to Sustainable Groundwater Management (Grombalia, NE Tunisia)

The Mediterranean coastal aquifer system of the Grombalia basin (NE Tunisia) offers immense potential as a source of fresh water for agriculture, industry, and drinking water supply. Nonetheless, due to its intricate hydrogeological characteristics and the prevailing issue of groundwater salinity, comprehending its groundwater system behavior becomes crucial for the effective and sustainable management of this aquifer system. Based on the hydrogeological characterization of the Grombalia basin, a novel 3D hydrogeological conceptual model was developed to enhance the understanding of its complex aquifer system. The integration of insights from geological, hydrogeological, hydrodynamic, and hydrochemical components facilitated the construction of the hydrogeological conceptual model. Although the model’s validity faced initial uncertainties due to spatial interpolation of lithological sequences, this study’s thorough and encompassing hydrogeological investigation overcame these limitations. As a result, a more informed comprehension of the aquifer system complexities was achieved. This study reveals that the basin is underlain by an extensive, cohesive Mio–Plio–Quaternary aquifer system. The model demonstrates vertical and lateral hydrogeological continuity between the Quaternary and underlying Mio–Pliocene deposits, enabling groundwater flow and exchange between these layers. Over-abstraction of the Mio–Plio–Quaternary aquifer system has led to a significant drop in piezometric levels and raised the risk of seawater intrusion. These findings emphasize the critical necessity of taking into account the interconnections among hydrogeological units to ensure sustainable groundwater management. The developed conceptual model offers a key tool for understanding the hydrodynamic functioning of the Grombalia aquifer system with a view toward guiding future groundwater management strategies. The application of this approach in the Grombalia basin suggests its potential applicability to other regional aquifers facing comparable challenges.

Calculation of Road Traffic Noise, Development of Data, and Spatial Interpolations for Traffic Noise Visualization in Three-dimensional Space

Road traffic noise visualization is vital in three-dimensional (3D) space. Designing noise observation points (NOPs) and the developments of spatial interpolations are key elements for the visualization of traffic noise in 3D. Moreover, calculating road traffic noise levels by means of a standard noise model is vital. This study elaborates on the developments of data and spatial interpolations in 3D noise visualization. In 3D spatial interpolation, the value is interpolated in both horizontal and vertical directions. Eliminating flat triangles is vital in the vertical direction. Inverse distance weighted (IDW), kriging, and triangular irregular network (TIN) are widely used to interpolate noise levels. Because these interpolations directly support the interpolation of three parameters, the developments of spatial interpolations should be applied to interpolate noise levels in 3D. The TIN noise contours are primed to visualize traffic noise levels while IDW and kriging provide irregular contours. Further, this study has identified that the TIN noise contours fit exactly with NOPs in 3D. Moreover, advanced kriging interpolation such as empirical Bayesian kriging (EBK) also provides irregular shape contours and this study develops a comparison for such contours. The 3D kriging in EBK provides a significant approach to interpolate noise in 3D. The 3D kriging voxels show a higher accurate visualization than TIN noise contours.

Enhancement of inverse-distance-weighting 2D interpolation using accelerated decline

Abstract Two-dimensional interpolation – or surface fitting – is an approximation tool with applications in geodetic datum transformations, terrain modelling and geoid determination. It can also be applied to many other forms of geographic point data, including rainfall, chemical concentrations and noise levels. The problem of fitting of a smooth continuous interpolant to a bivariate function is particularly difficult if the dataset of control points is scattered irregularly. A typical approach is a weighted sum of data values where the sum of the weights is always unity. Weighting by inverse distance to a power is one approach, although a power greater than 1 is needed to ensure smooth results. One advantage over other methods is that data values can be incorporated into the interpolated surface. One disadvantage is the influence of distant points. A simple cut-off limit on distance would affect continuity. This study proposes a transition range of accelerated decline by means of an adjoining polynomial. This preserves smoothness and continuity in the interpolating surface. Case studies indicate accuracy advantages over standard versions of inverse-distance weighting.

A modified radial basis function network integrating Mahalanobis distance and LASSO for soil mapping

In recent decades, machine learning (ML) has replaced geostatistics in soil mapping to overcome the lack of regional soil data. The radial basis function network (RBFN) is a classical ML method for spatial interpolation, but it has limitations in terms of anisotropy and flexibility in soil science. To overcome these shortcomings, we proposed a new model called RBFN-S by incorporating the Mahalanobis distance and the least absolute shrinkage and selection operator (LASSO) into the conventional RFBN, which are new developments in spatial interpolation. One simulated and two real cases were used for validation and investigation. The results showed that RFBN-S performed well in interpolating both Gaussian fields and non-stationary and non-Gaussian fields. In addition, the two introduced elements also played a significant role in improving the interpolation. The comparisons between RBFN-S and conventional RBFN, LASSO, and geostatistical methods demonstrate the advantage of RBFN-S in capturing correlations between soil properties and their location. Further development of the RBFN-S can be performed from the perspective of uncertainty quantification, optimization of meta-parameter tuning, and incorporation of environmental covariates.

Analisis Spasial Tingkat Intrusi Air Laut Pada Sumur Bor Di Kota Kendari

In simple terms, water is one of the natural resources that is very important and valuable for the survival of humans and other living things. The aims of this study are to: (1) Determine the distribution of the use of boreholes; (2) Knowing the level of sea water intrusion on ground water in Kendari City. The analytical methods used are: (1) Spatial interpolation analysis to create a map of the distribution of boreholes using the Arcgis 10.8 application; (2) Quality assessmentborehole water with seawater intrusion based on DHL values. The results of this study include: (1) The distribution of the use of drilled wells is spread throughout the Kendari City area with the highest distribution being in Poasia and Kambu Districts; (2) The groundwater condition of Kendari City has been very high intrusion with an area of 15,573,441.57 acre or 6%, these conditions are in Abeli District, Kendari District, West Kendari District, Mandonga District, Nambo District and Poasia District. The condition of groundwater with moderate intrusion level is 16% or 42.465,795.75 acre, the area with unintruded groundwater is in Baruga District.Keywords: Spatial Analysis, Seawater Intrusion, Deep well