Spatial Analysis Research Articles

Spatial and Temporal Analysis of China’s Environmental Regulatory Framework: A Multidimensional Assessment

Spatial and Temporal Analysis of China’s Environmental Regulatory Framework: A Multidimensional Assessment

Beyond the ice: exploring Antarctic soils research through spatial and scientometrics analysis

Abstract This spatial-scientometric study addresses research on Antarctic soils from 1958 to 2021. Through the review of 553 publications in the Web of Science and Scopus databases, geographical distribution, productivity, coauthorship and research topics were analysed. The results highlight the high productivity and interaction between researchers and institutions around the world, with a focus on microbiology, pollution, bioremediation, biogeochemistry and thermal and water monitoring of the soil and permafrost. This study provides insights into the importance of polar soils as global environmental indicators. The scientometric and spatial approach contributes to understanding the social and conceptual structure in this research area in addition to the development of the subject in time and space.

Refining soil nutrient assessment: Incorporating land use boundaries for precision agriculture.

Soil nutrient levels play a crucial role in determining crop yield. A comprehensive understanding of the spatial distribution patterns and evaluation grades of soil nutrients is of significant practical importance for informed fertilization practices, enhancing crop production, and optimizing agricultural land utilization. This study focuses on the urban area of Kashi Prefecture in Xinjiang as a case study. Utilizing soil sample data, GIS spatial interpolation analysis was conducted, incorporating plot boundary information to propose a comprehensive evaluation method for assessing soil nutrient levels at the plot level. Experimental findings revealed the following: (1) The average values of soil organic matter (SOM), total nitrogen (AN), total potassium (AK), and total phosphorus (AP) in the study area were determined to be 13.3 g/kg, 0.74 g/kg, 0.33 g/kg, and 0.03 g/kg, respectively. Among these, AN and SOM were classified as the fourth grade, indicating relatively deficient levels, while AK and AP were classified as the first and second grade, indicating relatively abundant levels. (2) The comprehensive evaluation of soil nutrient grades in the study area primarily fell within the third, fourth, and second grades, representing areas of 29.08 km2, 25 km2, and 4.05 km2, accounting for 50.03%, 43%, and 6.97% of the total area, respectively. (3) The evaluation results of soil nutrient levels at the plot level emphasized the boundary characteristics and provided a more refined assessment grade. This evaluation method is better suited to meet the practical production requirements of farmers and is considered feasible. The outcomes of this study can serve as a reference for precision agriculture management.

Numerical Simulation and Experimental Research of Spatial Volume Distribution and Velocity Changes of Abrasive Suspension Jet Based on LES + VOF + DEM

The machining accuracy and cutting ability of abrasive suspension jet (ASJ) are affected by the spatial volume distribution and velocity changes of jet flow and abrasives in the free jet area outside the nozzle. In this paper, large eddy simulation (LES) flow model, volume of fluid (VOF) multiphase flow model and discrete element model (DEM) were used to simulate the flow field characteristics of ASJ. The spatial volume distribution and velocity changes of jet flow and abrasives under 10[Formula: see text]MPa jet pressure were studied and the model validation experiments were carried out. In the comparison between the numerical simulation results and the experimental results, it was found that the average error of the percentage of central jet flow is 3.4%, the percentage of central abrasive particles is 3.0% and the velocity of abrasive particles is 4.2%. The physical structure of the numerical model is fixed (so it does not support abrasive injection jet (AIJ)), and the material physical parameters, initial conditions and boundary conditions are editable. Therefore, it can be used to predict the degree of water divergence, abrasives divergence and abrasives velocity attenuation of ASJ with different jet pressure and abrasive types. This numerical model can provide guidance for the parameter setting of ASJ machining process in order to obtain better machining accuracy and cutting ability.

Geospatial insights into open defecation vulnerability in Gulumbe, Nigeria

Open defecation (OD) poses a serious public health risk by exposing communities to diseases and compromising the well-being of people. One potential approach to mitigating its impact is the application of spatial planning techniques. However, spatial data paucity is one of the major problems in addressing open defecation issues in Nigeria. This study employed spatial analysis to identify areas particularly susceptible to OD using Geographical Information System (GIS) including Merginmap applications on Android and QGIS software, which were used with field observations to georeferenced residential areas, public toilets, and instances of open defecation. Proximity analysis and local indicators of spatial autocorrelation, based on Moran’s I, were employed to assess vulnerability. Buffer analysis was conducted to identify residential areas within 100 m of an OD site, and the findings indicated residential areas at high risk of experiencing negative impacts from OD. The LISA analysis revealed a high-to-high clustering pattern (p < 0.05) in the southern region of the study area, which aligns with the results of the buffer analysis and further suggests consistent spatial patterning. Furthermore, OD was more prevalent near water bodies and inner residential areas, increasing the risk of water and air pollution. This pollution could facilitate the spread of harmful bacteria, such as E. coli and Salmonella, from OD sites to nearby households, increasing the risk of disease. This study stresses the potential of GIS in evaluating areas vulnerable to OD and highlights the need to expand awareness campaigns regarding proper hygiene and sanitation practices in rural communities.

A practical approach to meeting national obligations for sustainable trade under CITES.

Reconciling conservation goals with sustainable resource use requires adaptive management strategies. The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) regulates global trade for species listed on Appendix II, partly by requiring member countries (parties) to ensure exports do not damage wild populations (called making positive "nondetriment findings" [NDFs]). Unfortunately, when parties find NDFs difficult, they often suspend legal trade, imposing economic costs and driving trade underground. To make it easier for parties to examine the detrimental nature of exports, we devised a spatial approach and applied it to seahorses (Hippocampus spp.) in Tamil Nadu, India, as an example. Our approach involves mapping answers to 5 key questions on species distribution (QA), pressures (QB), management measures (QC), management implementation (QD), and species' population status (QE). We gathered data from fisher interviews and published literature. Seahorse abundance was greatest in southern Palk Bay and the northern Gulf of Mannar, primarily in seagrasses and coral reefs (QA). Fishing pressure was highest in Palk Bay, primarily from bottom trawlers and dragnetters operating in shallow seahorse habitats near the coastline (QB). Management measures including a marine protected area (MPA), bottom trawl exclusion zone, and closed season were theoretically in place (QC), but their implementation was poor (QD). Fishers reported seahorse catches in 85% of the area covered by the MPA and the exclusion zone; bottom trawlers were responsible for most violations. Seahorses were also captured in Sri Lankan waters, where bottom trawling is banned. Fisher reports indicated declining seahorse catches and reduced body sizes (QE), highlighting unsustainable exploitation. Our results highlight the need for better implementation of existing management measures before a positive NDF can be made and suggest mitigation beyond bans. Such pragmatic spatial analyses can help regulate exports at sustainable levels, supporting CITES implementation for its vast range of species.

Family communal space as a natural surveillance function to create resilient coastal settlements

ABSTRACT Family communual space (FCS) can become a natural surveillance site for residents to monitor their environment. This study, of a fishing village in Indonesia, aims to determine the extent to which FCS plays a role as natural surveillance for its environment. The method uses a combination of quantitative (space syntax) with connectivity, visibility, and isovist approaches, as well as qualitative, such as support for data on kinship level, time of FCS use, and FCS location. The spatial analysis results show that visibility is low because the housing is dense and irregular, axial connectivity is low because there are many bends in roads, and the isovist has a wide observation range. The isovist was able to demonstrate the FCS’s role in natural surveillance. Findings: FCS and kinship ties can optimize environmental security even though visibility and connectivity are low to achieve resilience and sustainability and these are new prospects for researching the same thing but with different spatial methods or different places.

Water and socioeconomic inequalities: spatial analysis of water consumption in Brazil

ABSTRACT To achieve equitable water access, it is necessary to understand and quantify the heterogeneity of water consumption. Thus, this study assessed the spatial dependence of water consumption for Brazil and an urban center. Two spatial autocorrelation tests were applied: the Global Moran’s Index (I) and the Local Indicator of Spatial Association (LISA). It was found that, both at the national and municipal scales, water consumption is not uniformly distributed in space, indicating the formation of clusters of regions with High-High, High-Low, Low-High, and Low-Low aggregation patterns. In this way, water and socioeconomic inequalities in Brazil are highlighted, emphasizing the gap between the Center-South and the North-Northeast regions. Additionally, water and socioeconomic disparities within the city of Fortaleza are also observed, resulting from its urbanization process. Finally, the spatial autocorrelation between water consumption and per capita income is strongly evident at both the national and municipal levels.

Revisiting moated sites in Northeast Thailand: integrating spatial analysis with previous and new research insights

Moated sites in northeast Thailand serve as pivotal evidence of human–environment interaction since the Iron Age and have been subject to extensive study. This research conducts a comprehensive examination of moated sites, aiming to analyze their spatial characteristics and relationships with physical environmental factors across various scales, including regional and major river basins. To achieve these goals, advanced GIS tools including average nearest neighbor (ANN) analysis, kernel density estimation (KDE), standard deviational ellipse (SDE) analysis, and the Kruskal–Wallis test are employed. By incorporating a dataset of 468 moated sites and expanding the geographic scope beyond previous studies, this research offers new insights into settlement patterns and environmental interactions. Contrary to earlier interpretations suggesting random dispersion, the study’s findings reveal a clustering pattern, indicating intentional spatial organization by ancient communities. The study also highlights the influence of rainfall on the number of moats, with multiple moats more common in lower rainfall areas and single moats in higher rainfall regions. Additionally, the results observe that sites with more moats are typically found at higher elevations, although no significant correlation between elevation and site size was identified, challenging previous studies' conclusions. These results underscore the importance of local environmental conditions, such as rainfall and elevation, in shaping the distribution and characteristics of moated sites. The findings highlight the complex interplay between environmental factors and human settlement strategies, emphasizing the role of water management in ancient communities.

Geo-environmental monitoring of coastal and land resources for Coatzacoalcos coastal region

Coatzacoalcos is one of the notable Mexico coastal areas regarding location, activities, and resources. The purpose of the current study is to use geographic techniques to assess the spatial and temporal changes in Land Use/Cover (LULC), Land Surface Temperature (LST), and Shoreline as a result of anthropogenic and natural factors. Four calibrated multi-temporal Landsat images, dated 1993, 2003, 2013, and 2023, were combined and processed to accomplish this goal. The six primary LULC classes forest, built-up land, mining, water bodies, and agriculture were mapped using RF Machine learning algorithms. The last period (2003–2023) shows the highest rate of built-up land expansion, while the years 1993–2013 show the lowest rate of agricultural land reduction. Forest cover in the southeast decreased steadily from 339.25, 308.49, 287.43, and 211.83 km2 in 1993, 2003, 2013, and 2023. There is a slight difference in mean LST between built-up areas that recorded 26, 29, 32, and 33 °C in 1993, 2003, 2013, and 2023, respectively. The following is an order of LULC from lowest to highest LST during 2023 water body 12 °C, Forest 19 °C, Agriculture Land 22 °C, Mining 26 °C, Barren Land 34 °C and Built-up Land 33 °C. In Zone B near Coatzacoalcos showed particularly high accretion as a result of building artificial structures. Maximum rates of accretion and erosion were recorded at 2.0 m/year and −9.63 m/year, respectively. Fortunately, Zone C has minimal urban development and is home to forests and agricultural areas, so the potential negative effects are reduced. This study aids in understanding spatial and temporal LULC changes, LST variations, and shoreline dynamics, crucial for the sustainable management of Coatzacoalcos coastal resources in Mexico.

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q &gt; 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

The Application and Evaluation of the LMDI Method in Building Carbon Emissions Analysis: A Comprehensive Review

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.

Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method

The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development.

Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

Prevalence, spatial distribution and determinants of complete childhood pneumococcal conjugate vaccination in Ethiopia: spatial and multilevel analyses

IntroductionPneumococcal disease is a serious global public health concern. The primary causative agent of severe illnesses such as pneumonia, meningitis, acute otitis media, and bacteremia is the pneumococcus bacterium. The pneumococcal conjugate vaccine is a key strategy to reduce the burden of pneumococcal disease. Understanding the spatial distribution of complete childhood pneumococcal conjugate vaccine utilization and its associated factors is crucial for designing strategies to improve vaccination implementation. Therefore, this study aimed to determine the spatial distribution of complete childhood pneumococcal conjugate vaccination coverage and identify its determinants in Ethiopia.MethodA spatial and multilevel analysis was conducted using data from the 2019 Ethiopian Mini Demographic and Health Survey. The analysis included a total of 2,055 weighted children. The association between the outcome variable and the explanatory variables was determined by calculating adjusted odds ratios at a 95% confidence interval. Explanatory variables were considered significantly associated with the outcome if the p-value was less than 0.05.ResultThe prevalence of complete childhood pneumococcal conjugate vaccination in Ethiopia was 53.94% (95% CI: 51.77, 56.08). Higher complete childhood pneumococcal vaccination coverage was observed in the Addis Ababa, Tigray, Amhara, Benishangul-Gumuz, and Oromia regions, while lower coverage was seen in the Afar, Somali, and SNNPR regions of Ethiopia. Factors significantly associated with complete childhood pneumococcal conjugate vaccination included maternal age, antenatal care visits, place of delivery, region, community women’s literacy level, community poverty level, and community antenatal care utilization.ConclusionThe distribution of complete childhood pneumococcal conjugate vaccination exhibited spatial variability across Ethiopia. Approximately half of children aged twelve to thirty-five months received the full dose of the childhood pneumococcal conjugate vaccine in the country. Several factors were identified as statistically significant determinants of complete childhood pneumococcal conjugate vaccination, including maternal age, antenatal care visits, place of delivery, region, community women’s literacy level, community poverty level, and community ANC utilization. Therefore, policies and strategies aimed at combating pneumococcal disease should consider these determinants and address areas with low vaccination coverage.

Equity in Public Facilities Provision in an Urban Area: A Spatial Analysis of Rajshahi City Corporation

Equity in Public Facilities Provision in an Urban Area: A Spatial Analysis of Rajshahi City Corporation

Pattern and change of NDVI and their environmental influencing factors for 1986–2019 in the Qinling-Daba Mountains of central China

Previous studies have shown that climate change and human activities play an important role in the vegetation dynamics in the Qinling-Daba Mountains of central China. However, which environmental factors including climate, topography, soil and human activities play an important role in the vegetation dynamics and its spatial pattern in the Qinling-Daba Mountains remains to be further clarified. Based on the normalized difference vegetation index (NDVI) data of the growing season from 1986 to 2019 synthesized by Landsat series satellite data on Google Earth Engine, this study aimed to further investigate the spatial pattern of NDVI and its dynamics, and clarify its environmental controlling factors in the Qinling-Daba Mountains using the methods of spatial analysis and Geodetector. The results showed that: (1) the spatial pattern of NDVI in the study area had a U-shaped NDVI distribution in latitude, anti-U-shaped patterns in longitude and with increasing altitude. (2) 2005 was the year of NDVI breakthrough increase, and the vegetation dynamics was divided into two periods according to the result of MK mutation test: the slow increasing period with an increasing rate of 0.25%/a from 1986 to 2004 (R2 0.74), and the rapid increasing period with an increasing rate of 0.30%/a from 2005 to 2019 (R2 0.92). (3) Topography regulating local hydrothermal conditions and soil enriching nutritions played more important influence on NDVI spatial pattern than climate factors (temperature and precipitation) at the regional scale. The effect of land use on NDVI change was stronger than that of climate warming (temperature), and the climate warming in recent decades played a more important role than precipitation on the NDVI dynamics. Research on vegetation patterns, changes and their environmental influencing factors will help the government and other related agencies to formulate plans or policies for infrastructure development and land management, ecological restoration.

Characterization of tumor heterogeneity through segmentation-free representation learning.

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

Spatial Analysis with Detailed Indoor Building Models for Emergency Services

This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations.

Environmental Impact of River Bank Erosion on Stream Morphology: A Study of the Mini Piti Stream, Obio-Akpor, Nigeria

This study was designed to assess the impact of river bank erosion on the environment and the channel morphology for the Mini-Piti stream in Obio-Akpor local government area of Rivers State, Nigeria. This study determined the depth variations from width measurements due to bank erosion activities and the impact of indiscriminate waste dumps on the river channel. Physical water quality parameters of temperature, turbidity, transparency and TSS were determined using standard methods. The stream was divided into three stations; upstream 2.5km, midstream 2.5km, and downstream with intervals of 1km measured to differentiate each station as 5 samples were obtained per station at intervals of 0.5km apart. The flow velocity and discharge were estimated as the average depth recorded upstream being DS 0.9m, WS 1.4m, Midstream DS 1.3m, WS 1.7m, and Downstream = DS 1.5m WS 1.7m. The maximum width of 11m and minimum of 0.1m were recorded though natives confirmed that the stream is closing up unless maintenance dredging to expand the depth and width, the loss of this channel cannot exceed 20 years. The field observation and the impacts of precipitation and storm water from the built area of midstream were very intensive whose action increased the water level above its channel capacity and eroded the bank. From t-test analysis, there were significant seasonal variations in the depth for the measured widths for the upstream T (8) =2.1, p= 0.036 and the midstream, T (8) =2.36, p=0.023. Similarly using the one-ANOVA, a significant variation in the depths of five locations during the dry season downstream F (4, 60) = 2.79, p= .03 was recorded. The Tukey’s HSD post hoc test showed the locations have no significantly high depths for the studied widths sizes (p&lt;0.01 and p&lt;0.05). A one-way ANOVA revealed a significant variation in the depths of five locations during the wet season upstream F (4, 60) = 5.966, p= .000. The Tukey’s HSD post hoc test showed the locations have significantly high depths for the studied widths sizes (p&lt;0.01 and p&lt;0.05) at locations L2:L4 at Q=5.53 (p=0.00216, L2:L5 at Q= 4.72 (p=0.1214), L3:L4 at Q=4.90 (p=0.00841) and L3:L5 at Q=4.10 (p=0.0403). The studied physical parameters were all above WHO permissible standards and there were no significant variations between dry and wet seasons (38% turbidity midstream, TSS-33%, 31% and 30.6% variation upstream, midstream and downstream respectively) as well as spatial changes. The collapsed sediments and waste dumped at the bank of the stream were transported downstream due to reduced velocity and coarser sediments trapped at the stream bed causing shallow depth while the suspended fine sediment on the water column reduced the clarity of the stream resulting in adverse flooding over 20 years now. The research revealed that bank erosion has significant impact on water quality due to poor transparency and geometry of stream by the alterations of the channel depth and width, flow rate, scouring and deposition of sediments in the stream. The need for regulatory agencies to control anthropogenic inputs and enhance bank erosion prevention programmes cannot be over-emphasized. The monitoring of indiscriminate urbanization programmes to limit excess scouring activities by National Inland Waterways Authority (NIWA) is very essential to reduce excessive sand mining.