Spatial Statistical Techniques Research Articles

Settlement Patterns of the Population Use the Average Nearest Neighbor Method

This study utilizes the Average Nearest Neighbor (ANN) method to analyze settlement patterns based on population density data across 15 regencies and cities in Lampung Province, Indonesia. ANN is a spatial statistical technique used to determine spatial distribution patterns by calculating the average distance between points, the number of points, and the area of the region. The analysis involved computing the ANN index and z-scores for each regency and city. The results, as shown in the analysis, indicate that the ANN index values are below one, ranging from 0.114 to 0.769, and the z-scores are negative, ranging from -146.719 to -13.235. These findings suggest that the settlement patterns across all studied areas in Lampung Province exhibit a clustered pattern. This implies that densely populated areas do not significantly influence the settlement patterns of less densely populated regions. The study concludes that the population density pattern in Lampung Province is predominantly clustered, reflecting a tendency for settlements to be concentrated in specific areas rather than dispersed.

Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data.

The agglomeration and dispersion of tourist attractions in space greatly affect the development of regional tourism resources and the consumption choice of tourism market. At present, the research on the spatial distribution characteristics of tourist attractions and their influencing factors mainly adopts induction and investigation, and there is a lack of effective statistical models for the research on the spatial distribution of tourist attractions and their influencing factors in some historical and cultural ancient cities. This paper uses Internet technology to obtain the spatial distribution data of tourist attractions in Shaoxing city, and uses mean nearest neighbor analysis, nuclear density analysis, imbalance index analysis, standard deviation ellipse and other spatial statistical analysis techniques and geographical detector methods to study the spatial distribution characteristics and influencing factors of tourist attractions in Shaoxing City. This paper studied the distribution characteristics of tourist attractions in Shaoxing city, such as spatial aggregation, distribution equilibrium and spatial orientation, and applied geographical detector to study the influencing factors of the spatial distribution of scenic spots. It was concluded that the spatial distribution pattern of scenic spots was affected by various factors such as natural environment, social environment and economic environment. The explanatory power of two-factor interaction is obviously stronger than that of single factor. The research results provide scientific basis for the planning, layout and development of tourist attractions in Shaoxing and its similar cities, and then promote the high-quality development of tourism in Shaoxing and its similar historical and cultural ancient cities.

The Processing Spatial Data for Statistical Modeling and Visualization Case study: INLA model for COVID-19 in Alabama, USA

This research emphasizes the visualization of spatial data for statistical modelling and analysis of the relative risk associated with the COVID-19 pandemic in Alabama, USA. We used Bayesian analysis and the Integrated Nested Laplace Approximation (INLA) approach on data ranging from March 11, 2020, to December 31, 2022, which included observed COVID-19 cases, the population for each of the Alabama counties, and a Geographical map of the state. The geographical distribution of COVID-19’s relative risk was determined using various spatial statistical techniques, indicating high-risk locations. The study used Besag-York-Mollié (BYM) models to assess the posterior relative risk of COVID-19, and it found a statistically significant average decrease in COVID-19 case rates across the 67 counties evaluated. These findings have practical implications for evidence-based policymaking in pandemic prevention, mitigation, and preparation.

Unraveling the spatial imprint of hominin and carnivore accumulations in Early Pleistocene African sites

Reconstructions of palimpsest formation and dynamics in Early Pleistocene African archaeological deposits have undergone significant advances thanks to taphonomic research. However, the spatial imprint of different agents implicated in most of these accumulations still needs to be addressed. We hypothesize that different site formation dynamics may yield diverse spatial distributions of archaeological remains, reflecting the intervention of different agents (i.e., hominins, felids, hyaenids) in palimpsests. This study aims to investigate the spatial patterns of archaeological remains in a selected sample of Early Pleistocene accumulations with the goal of understanding and characterizing their spatial dynamics. Building on previous taphonomic interpretations of twelve paradigmatic archaeological deposits from Olduvai Bed I (FLK Zinj 22 A, PTK 22 A, DS 22B, FLK N 1–2 to 5, FLK NN 3, DK 1–3) and Koobi Fora (FxJj50, FxJj20 East and FxJj20 Main), we explore the spatial patterns of remains statistically and use hierarchical clustering on principal components analysis (HCPC) to group the highest-density spots at these sites based on a number of spatial variables. The results of this approach show that despite sharing a similar inhomogeneous pattern, anthropogenic sites and assemblages where carnivores played the main role display fundamentally different spatial features. Both types of spatial distributions also show statistical differences from modern hunter-gatherer campsites. Additional taphonomic particularities and differing formation processes of the analyzed accumulations also appear reflected in the classifications. This promising approach reveals crucial distinctions in spatial imprints related to site formation and agents’ behavior, prompting further exploration of advanced spatial statistical techniques for characterizing archaeological intra-site patterns.

Understanding impact of urban sprawl over sanitation risks using GIS‐based multicriteria decision‐making approach

AbstractUrban sprawl and the shortage of proper sanitary infrastructures significantly jeopardize public health and urban sustainability. The problem is further aggravated as a result of the rapid urbanization and urban sprawl. This study investigated the relationship between urban sprawl and sanitation risk conditions in a rapidly growing city in India. This was accomplished by investigating changes in urban sprawl areas between the periods 2000–2020 using multispectral satellite images and Shanon's entropy model and studying the pattern of spatial variations in basic sanitation services derived from the 100 household‐based surveyed WASH (water availability, sanitation, and hygiene) data collected in 2018 before COVID‐19 from 45 sprawl regions. Spatial statistical techniques, namely, the inverse distance weighted (IDW) interpolation and the multicriteria decision technique, were employed for neighborhood analysis and assessing sanitation risks inside the sprawl region. Results showed that Raipur exhibited urban sprawl and around 93.68% of the sprawl area was classified between high (6.47%)‐ and medium (80.52%)‐risk zones.

Transport fare and road traffic crashes in Nigeria: insights from a geographical analysis

Road traffic crashes (RTCs) are significantly high in Nigeria with serious social and health consequences. While existing studies on RTCs have mainly focused on the effect of socio-economic, environmental, human and mechanical factors to address the high rates, the relationship between road transport fares and RTCs has been glossed over in literature. Thus, this study examines the influence of road transport fares and other covariates on RTCs. Data on RTCs and the predictors between 2017 and 2022 were obtained from the records of the National Bureau of Statistics and the Federal Road Safety Corps. Spatial statistical techniques were used for the data analysis. RTCs vary across the country, and Northern Nigeria is the hot spot. Results from the spatial analysis show that road transport fares, population density, and illiteracy rate are significant predictors of RTCs. The study recommends striking a balance between fare affordability, the quality of service provided, and the implementation of effective transportation strategies.

Statistical Analysis of Radioactivity: Lamprecht Uranium Mine in Texas.

The former Lamprecht uranium mine facility in Texas ceased operations well before the new millennium. However, decommissioning activities were never completed by the licensee. Consequently, a legal proceeding was authorized between state and licensee representatives. Meanwhile, state funds were used to hire an independent contractor to perform radiological surveys and assess the magnitude of residual radioactivity across the terrain at the site. The purpose of this study was to apply advanced spatial statistical methods to the survey data measured by contractors at the Lamprecht site to precisely predict remaining radioactive hotspot locations post soil remediation activities. To accomplish this, descriptive statistics such as Google maps and boxplots along with inferential spatial statistical techniques, e.g., kriging and semivariograms, were employed. R coding was also used throughout. Specifically, the descriptive statistical methods included geographical mapping of targeted areas at the site coupled with summary statistics. Inferentially, spatial analytical techniques were employed to pinpoint the locations of elevated radiation levels above regulatory limits. Our results suggest that fewer hotspots were identified after remediation activities were completed at the site. This study provides an additional analytical resource for the State of Texas regarding the release of this former in situ leach uranium mine site to landowners for unrestricted use.

Land Use and Cover Changes in Brazilian Biomes between 1985 and 2018

In the last decades, there have been changes in land use and cover in the Brazilian territory, many resulting from anthropic activities. However, these changes impact the environmental system, compromising biodiversity and ecological services. In this context, integrative geographic information about environmental changes in the Biomes allows the identification of the main Spatio-temporal changes in land use and cover and can contribute to efficient environment planning. Thus, this paper proposes to describe and synthesize the Spatio-temporal changes in Brazilian Biomes between 1985-2018 using spatial statistics techniques and Principal Component Analysis with data from the MapBiomas project. It has employed the Google Earth Engine and QGIS for the analysis, with emphasis on spatial statistics and Principal Components Analysis which enabled the identification of the main changes in land use and cover. From the Principal Components Analysis, it was possible to reduce the data set with 34 images to two Principal Components that represent significant percentages of the variance of the original database. The results showed that the Brazilian Biomes had undergone significant changes over 34 years. The main vectors that drove these transformations were anthropic activities, such as urban growth, the advancement of agriculture, and cattle ranching in the countryside. Amazon and Cerrado present the largest area altered in square kilometers. And the fastest changes occurred on Amazon, Cerrado, and Pantanal. This work presents a synthesis of the main patterns and spatial-temporal changes in the use and cover of Brazilian Biomes, which can assist in the national environmental planning.

Why do parents refuse to self-protect? Socio-cultural factors influencing the immediate response of the older generation to COVID-19 and their spatial heterogeneity in China

Immediately after the initial COVID-19 outbreak in January 2020 in China, an intriguing trend of distrust and anti-protection among the older generation appeared. Based on 1926 samples collected across the Chinese mainland within a short timeframe, this paper adopted a series of global and spatial statistical techniques to explore the socio-cultural reasons underlying the older generation's risk response and their spatial heterogeneity in China. First, the global Ordinary Least Square (OLS) model validated the significant effects of trust in government information, cultural traditions, and social conformity, but the spatial autocorrelation (Moran's I) test of the residuals suggested spatial nonstationarity of the model. Second, the results from the Local Indicators of Spatial Association (LISA) and Geographically Weighted Regression (GWR) confirmed the spatial heterogeneity of the older generation's protective patterns. An ‘inverted T-shaped’ high-protection belt centering on Wuhan City was identified, indicating that physical proximity to and social connectedness with the pandemic epicenter is crucial in motivating the older generation's protection. In areas distant from and less connected with the risk center, strong collectivistic values may play a vital role in stimulating trust in government information and fostering positive protective intentions of the older generation. However, in most areas, such strong government trust is absent, and the inhibitory cultural and social factors often prevail to result in an anti-protection norm of the older generation. Finally, suggestions were provided to develop locally effective risk management measures from the socio-cultural perspective, and more geographic investigations on public risk response were proposed.

Spatial Distribution and Cluster Analysis of Dengue In Tamil Nadu, India 2017 – 2019

Background: Dengue, a viral infectious disease, is constantly reemerging and is spread by mosquitoes. It is most common in tropical and subtropical areas of the world, particularly in the Asia-Pacific region. A Surveillance study was carried out in the state of Tamil Nadu-India, from 2017 to 2019 to better understand the epidemiology and geographic spread of dengue. Materials & Methods: Geographical information system (GIS) software was used to map the district-wise endemicity. To pinpoint dengue hotspots and coldspots, spatial statistical analysis techniques like Getis-Ord Gi* were used. To comprehend the endemicity and clustering patterns in the state, self-organising maps (SOM)was also used. Results: Dengue hotspots are Chennai, Thiruvallur, Kancheepuram and cold spots are Cuddalore and Ariyalur for across three years. The SOM divides 30 districts into six clusters. The 10 districts are identified as extremely high endemic cluster and 9 districts as very high endemic cluster. These SOM clusters were projected geographically, and the districts were divided based on the intensity of the cases. Conclusion: SOM-GIS, a visualization technique, identifies hot spots and endemic clusters which helps in prompt decision-making for disease management by public health personnel.

An ecological study of the spatiotemporal dynamics and drivers of domestically acquired campylobacteriosis in Ireland, 2011-2018.

In 2021, Campylobacteriosis was the main gastrointestinal disease in the European Union since 2007 according to the European Centre for Disease Prevention and Control. In the Republic of Ireland, the incidence of the disease is particularly high with approximately 3,000 cases per annum, raising significant concerns for national health authorities with an expected increase in the number of cases in the light of climate change. The current study sought to assess the spatio-temporal patterns of campylobacteriosis in the Republic of Ireland using 20,391 cases from January 2011 to December 2018. An ensemble of spatial statistics techniques including seasonal decomposition, spatial clustering and space-time scanning, were used to elucidate the main individual and spatio-temporal characteristics of the disease in the country. Findings revealed that cases from the paediatric age group (i.e., under 5 years old) were more likely to occur in rural areas (aOR: 1.1.27, CI 95% 1.14-1.41) while cases from the intermediate age group (i.e., >5 & <65 years old) were associated with urban living (aOR: 1.30, CI 95% 1.21-1.4). The disease exhibited a peak during Irish summer, with a stronger seasonal signal reported in counties located on the Western part of the country. Infection hotspots were more likely to occur in urban areas, and more particularly on the Southern part of the island and around the main metropolitan areas. Overall, research findings pointed out the influence of local and spatio-temporally specific socio-demographic and environmental risk factors (i.e., cooking habits, local weather, dietary types) therefore highlighting the need for initiating spatio-temporally targeted health management and surveillance strategies.

Comprehensive Assessment of Vulnerability to Storm Surges in Coastal China: Towards a Prefecture-Level Cities Perspective

China is one of the countries that suffers severe damage from storm surges. Assessing the vulnerability to storm surges holds great significance for promoting sustainable development and minimizing disaster losses in coastal areas. This study first developed a vulnerability index by integrating 15 indicators from three components (exposure, sensitivity, and adaptability) that provide a comprehensive portrayal of the multidimensional structure of vulnerability. Subsequently, the vulnerability of Chinese coastal areas was comprehensively evaluated from the perspective of prefecture-level cities using a weight combination strategy. Furthermore, spatial statistical techniques were utilized to analyze the spatial heterogeneity of vulnerability. The results show that 64% of coastal cities are classified as being in the very high and high vulnerability categories, with Zhanjiang, Lingao, Dalian, Yancheng, and Shanwei exhibiting the highest vulnerability levels. Among the provinces, Guangxi and Hainan Provinces demonstrate the highest vulnerability, with more than 90% of their coastal cities facing high vulnerability. Additionally, the vulnerability of Chinese coastal cities exhibits significant spatial heterogeneity. Specifically, coastal cities located in the Yangtze River Delta and the Pearl River Delta regions are identified as low–low (LL) vulnerability clusters, whereas high–high (HH) vulnerability clusters are observed in coastal cities within the Beibu Gulf region. These results provide valuable insights for the formulation of disaster reduction policies at the provincial level and the focus for action at the local level.

Spatial statistical methodologies in COVID-19 Studies: A systematic review

Objective: As of August 2023, COVID-19 had claimed 7 million lives, making it the pandemic with the highest mortality rate. Therefore, The use of cutting-edge technologies and methods is essential when battling the COVID-19 epidemic. This paper aims to systematically review and synthetize applications of spatial statistical methodologies in the analysis of COVID-19. Material and Methods: 55 articles in total were screened from four main digital databases including Web of Science, SCOPUS, PubMed/MEDLINE, and Google schoolar. Three distinct concerns with the use of spatial statistical techniques in the analysis of COVID-19 are discussed, namely (i) applications of spatial regressions in the evaluation of COVID-19's effects, (ii) COVID-19 mapping using of hotspots and spatial clustering analyses, and (iii) applications of interpolation and geostatistics on COVID-19 studies, respectively. Results: Spatial regressions can support the assessment of the COVID-19 impacts on social-economy and environment. Whereas, hotspots and spatial clustering analysis can help effectively on COVID-19 mapping. Last but not least, geostatistics and interpolation are crucial for predicting COVID-19. Conclusion: This review not only emphasises the significance of spatial statistical techniques in COVID-19 studies, but it also sheds light on the practical applications of spatial statistics in COVID-19 research.

Recyclable waste in Brazilian municipalities: A spatial-temporal analysis before and after the national policy on solid waste

In 2010, the Brazilian authorities approved the National Policy on Solid Waste (NPSW) with the aim of promoting the recycling and reuse of solid waste. However, assessing the distribution and progress of recyclable material recovery following the implementation of this key government policy remains a challenging task. Therefore, this study employs a spatial-temporal analysis to investigate the impact of the NPSW on the recovery of recyclable waste in Brazil. We employed spatial statistical techniques such as Getis-Ord Gi* and Global Moran's I. The results revealed significant and heterogeneous clustering of recycled materials among municipalities, both before and after the implementation of the NPSW. The analysis demonstrated that recycled waste tended to cluster in more developed and urbanized areas, while a considerable number of cold spots were observed in less developed regions or densely populated areas, indicating low recycling rates. These findings raise environmental concerns for policymakers and emphasize the need for interventions to enhance recycling efforts. The study highlights the urgency of improving municipal solid waste management and raising awareness among local communities about the economic and environmental benefits of recycling, particularly in areas characterized by unsanitary living conditions, poverty, and low-income disparities. Therefore, it is recommended to tailor the legal framework to local realities, considering the vast and socioeconomically diverse landscape of Brazil.

Spatial analysis of vaccine coverage in children under the age of 1 year by mesoregions in Paraíba a northeastern Brazilian state.

Immunization is one of the most effective measures in public health, and it is responsible for the reduction of vaccine-preventable diseases. In the present study, vaccine coverage (VC) and the spatial dynamics of homogeneity of VC (HVC) were compared and analyzed in the terms of the immunobiologicals administered to children aged < 1 year in a state in Paraíba, Brazil. This is a mixed ecological study that used public-domain secondary data from the years 2016 and 2017 from the Information System of the Brazilian National Immunization Program (SI-PNI) and the Brazilian National Information System of Live Births (SINASC). VC rates were calculated by dividing the number of administered doses by the number of live births. Then, VC was classified into four categories. The Municipal HVC was considered adequate when the overall VC exceeds 75%. The study included a descriptive analysis and a spatial autocorrelation analysis for HVC using global and local Moran's statistics. The stratified VC analysis revealed a significant number of municipalities in each of the state's mesoregions with low or very low VC rates for all immunobiologicals, with the Mata Paraibana mesoregion having the worst percentages in both years studied. The spatial analysis of HVC revealed several clusters of inadequate homogeneity, with Mata Paraibana being the worst mesoregion in 2016. The analysis of spatial dynamics and spatial statistics techniques allows the precise identification of vulnerable areas, "vaccination pockets," making it possible to develop plans aimed at meeting the targets of the PNI.

Spatiotemporal Analysis of Land Use Land Cover Mapping and Change Detection in Dambatta Local Government Area

This research studied the spatiotemporal changes in land use (LU)/land cover (LC) in Dambatta local government area, with a view to identifying the effect arising from the observable changes in land use patterns. The imageries used in the study were obtained from the National Space Research and Development Agency (NARSDA), Abuja. Spatial analytical techniques and descriptive statistical techniques were employed to analyze the data. The results showed 66.8% reduction in agricultural lands, 45.5% reduction in vegetation cover, 223.2% increase in built-up areas, 269.1% increase in bare lands and 70% increase in water bodies within the 20 years. Spatio-temporal analysis of the three imageries revealed that agricultural lands were largely been taken over by urbanization while vegetation had rapidly given way to bare lands within the 20 years. It was observed that these changes resulted from anthropogenic activities, environmental factors and climate change. These result in the loss of farmlands, inadequate food supply, unemployment, inadequate industrial raw materials, reduction in revenue generated, forest depletion, desertification, wildlife extinction and temperature increase. While it is recommended that reforestation, land reclamation and irrigation agriculture should be promoted in the area, it is also suggested that further research should focus on the impact of climate change on land cover change in the area.

The dynamics and uniqueness of semigration to hermanus from across South Africa

Covid-19 significantly impacted the South African economy, particularly through geographical flexibility in workspaces, made possible by improvements in smart office technology. The ability to participate in hybrid forms of work (and ‘work from home’) has increased patterns of decentralised living, moving away from larger cities towards peripheral suburban locations and smaller towns, usually with the aim of improving living conditions. This trend has been described as ‘semigration’, and towns in the Western Cape seem to be the prime locations. The aim of this study is to investigate the extent and characteristics of semigration to Hermanus, in the Western Cape of South Africa. The data for this study was collected from two companies (Lightstone and Property24). Sampled data from Lightstone was analysed through frequency counts to provide a trend analysis of semigration to Hermanus. Property24’s open-source data was analysed through frequencies, crosstabulations, and various spatial statistical techniques in ArcMap. Results indicate that Covid-19 slowed the pace of property purchases in Hermanus but did not necessarily increase this pace drastically in comparison to previous years. This offers only some anecdotal evidence of potential semigration but does not necessarily confirm it. Geographic shifts were however observed in locations where properties were purchased, while property prices increased in certain suburbs but decreased in others. These variations, together with changing characteristics of semigrants (i.e., their origin locations, wealth segments, and age groups) could suggest changing demographic patterns associated with the semigration phenomenon. Significant impacts on municipal policies and strategies were identified and suggestions made, along with recommendations for further research.

Assessment and Spatial Analysis of Land Use and Land Cover (LULC) Dynamics in the Black Volta Basin

The Black Volta Basin, a critical hydrological region, is experiencing rapid changes in Land Use and Land Cover (LULC) patterns due to diverse anthropogenic and environmental factors. This study employs a comprehensive Multi-Sensor Remote Sensing and Geographic Information System (GIS) methodology to assess and analyze the spatiotemporal dynamics of LULC in the basin. Utilizing data from multiple remote sensing platforms, including optical and radar sensors, as well as historical GIS datasets, a detailed LULC classification is performed. The integration of multi-temporal satellite imagery enables the detection and quantification of changes across distinct land cover categories. Through rigorous spatial analysis and statistical techniques, the study reveals the extent and magnitude of LULC transformations over a defined period. Land cover transitions are identified, characterized, and linked to potential drivers such as urbanization, agricultural expansion, and natural processes. The outcomes of this research contribute to an enhanced understanding of the evolving LULC dynamics in the Black Volta Basin, shedding light on both natural and human-induced changes. The results underscore the significance of a multi-sensor approach in accurately capturing complex LULC changes and their implications for regional sustainability and resource management. This study's insights serve as valuable inputs for policymakers, researchers, and stakeholders engaged in land-use planning, environmental conservation, and sustainable development within the Black Volta Basin and similar ecologically sensitive areas.

A Spatial Model of the Social Vulnerability Index for Vaccine COVID-19 in Java, Indonesia

The COVID-19 vaccine coverage in Indonesia remains low, with uneven distribution across Java, while COVID-19 cases continue to pose a public health concern. This study seeks to develop a spatial model using the Social Vulnerability Index (SVI) approach to identify the spatial pattern of COVID-19 vaccination and the factors influencing it in Java. The study adopts an ecological design with a spatial approach, encompassing 118 districts/cities. The dataset used in this research focuses on the coverage of COVID-19 vaccination for the second dose, spanning from March 15, 2021, to January 11, 2022. Spatial statistical techniques such as spatial autocorrelation and Geographically Weighted Regression were employed to analyze the data. The findings reveal that the Human Development Index, unemployment rate, and housing conditions significantly impact the spatial distribution of COVID-19 vaccine coverage, indicating the presence of spatial interaction among regions. Socioeconomic factors emerged as key variables influencing the study outcomes. Given that enhancing the community's economy requires time, interventions tailored to the prevailing conditions are necessary. Therefore, interventions to increase COVID-19 vaccine coverage should prioritize health promotion efforts, particularly in areas with low socioeconomic conditions.

Static and dynamic source identification of trace elements in river and soil environments under anthropogenic activities in the Haraz plain, Northern Iran

Unsustainable human activities have disrupted the natural cycle of trace elements, causing the accumulation of chemical pollutants and making it challenging to determine their sources due to interwoven natural and human-induced processes. A novel approach was introduced for identifying the sources and for quantifying the contribution of trace elements discharge from rivers to soils. We integrated fingerprinting techniques, soil and sediment geochemical data, geographically weighted regression model (GWR) and soil quality indices. The FingerPro package and the state-of-the-art tracer selection techniques including the conservative index (CI) and consensus ranking (CR) were used to quantify the relative contribution of different upland sub-watersheds in trace element discharge soil. Our analysis revealed that off-site sources (upland watersheds) and in-site sources (land use) both play an important role in transferring trace elements to the Haraz plain (northern Iran). The unmixing model's results suggest that the Haraz sub-watersheds exhibit a higher contribution to trace elements transfer in the Haraz plain, and therefore, require greater attention in terms of implementing soil and water conservation strategies. However, it is noteworthy that the Babolroud (adjacent to Haraz) exhibited a better performance of the model. A spatial correlation between certain heavy metals, such as As and Cu, and rice cultivation existed. Additionally, we found a significant spatial correlation between Pb and residential areas, particularly in the Amol region. Our result highlights the importance of using advanced spatial statistical techniques, such as GWR, to identify subtle but critical associations between environmental variables and sources of pollution. The methodology used comprehensively identifies dynamic trace element sourcing at the watershed scale, allowing for pollutant source identification and practical strategies for soil and water quality control. Tracer selection techniques (CI and CR) based on conservatives and consensus improve unmixing model accuracy and flexibility for precise fingerprinting.