GIS Watershed Polygon: Correlation Data Analysis (Lab Report Sample)

* INTRODUCTION Watershed management is crucial for ecosystem health and the sustainability of water supplies. Geographic Information Systems (GIS) are useful tools in this domain because they enable the analysis and visualization of spatial data connected to watersheds. One critical aspect of GIS in watershed management is the creation of watershed polygons, which indicate the spatial extent of catchment areas. Correlation data analysis inside GIS frameworks can reveal information about the correlations between various hydrological and environmental characteristics in distinct watersheds: LS (Length-Slope factor), NDVI (Normalized Difference Vegetation Index), MRP (Mountain Ridge Proximity), Slope, SPI (Stream Proximity Index), and TRI (Topographic Ruggedness Index). This study uses watershed polygon GIS data, as well as correlation data analysis, to analyze the interactions and dependencies between various hydrological factors. The information that is provided offers a strong basis for investigating these correlations because it contains several measurements from different watersheds. The analysis includes calculating correlation coefficients between these attributes in order to determine the degree and direction of their correlations. This method aids in determining which factors are most closely related and have the capacity to influence watershed dynamics. Objective The main objective aims to make use of Geographic Information System (GIS) data for the analysis and understanding of correlations within a dataset of watershed polygons. * To examine the dataset containing measurements of watersheds across various IDs and time periods. * To conduct correlation analysis on the dataset, aiming to identify relationships and dependencies between different variables using Microsoft Excel * To visualize the result using suitable graphical representations such as scatter plots, heat maps, or correlation matrices. * To interpret the correlation findings in order to gain insights into the spatial and temporal dynamics of the watershed data. This may involve identifying significant correlations and potential causative factors. * METHODS AND MATERIALS Materials The materials used in this analysis included the GIS Watershed Polygon dataset, which contained multiple variables across different IDs and columns, and Microsoft Excel software for performing the data analysis. Methods This methodology facilitated a detailed examination of the relationships between various environmental variables in the watershed. The analysis, conducted using Microsoft Excel with 57 data points for each variable, aimed to gain insights into how these variables interact and inform improved watershed management strategies. Data preparation involved importing the data into Excel, ensuring proper column labels, and organizing each variable into its corresponding column with records aligned by respective IDs. The Pearson correlation coefficient was calculated for all variable pairs using Excel's Data Analysis tool with the Correlation function. This resulted in a correlation matrix that displayed the strength and direction (positive or negative) of the relationships between all possible variable combinations. The analysis focused on summarizing the significant correlations identified. This provided valuable insights into the interdependencies between various watershed attributes, contributing to a deeper understanding of the underlying patterns and potential factors influencing overall watershed characteristics. * RESULTS The analysis involved the calculation of correlation coefficients between various hydrological and environmental parameters. The parameters analyzed include LS (Length-Slope factor), NDVI (Normalized Difference Vegetation Index), MRP (Mountain Ridge Proximity), Slope, SPI (Stream Power Index), and TRI (Topographic Ruggedness Index). Table 1: Correlation-Covariance Watershed Polygon data -46355142240 Table 2: Correlation-Covariance Watershed Polygon Results -36830130175 IIV. DISCUSSION This discussion focuses on the correlation analysis of various watershed polygon GIS data variables including LS (Length-Slope), NDVI (Normalized Difference Vegetation Index), MRP (Mean Rainfall Precipitation), Slope, SPI (Stream Power Index), and TRI (Topographic Ruggedness Index). The analysis was conducted using Excel to calculate the correlation coefficients, which indicate the strength and direction of the relationships between these variables. Table 3: Positive Correlations (Correlation Coefficient of 1) 55245100330 In the dataset, some variable pairs show perfect positive correlations, meaning they increase in perfect synchrony. For instance, LS1 with LS1, LS4 with LS4, and NDVI1 with NDVI1 all have a correlation of 1, which is expected since each variable is perfectly correlated with itself. Additionally, LS4 and LS3 have a high positive correlation of 0.896, MRP2 and MRP1 have 0.870, Slope2 and TRI2 have 0.985, and SPI4 and LS4 have 0.999, indicating strong relationships between these variables. Table 4: Negative Correlations (Correlation Coefficient of -1) 9779086995 Strong negative correlations exist in the dataset, close to -1, indicating that as one variable increases, the other decreases. For example, LS4 shows strong negative correlations with LS1 (-0.484) and LS3 (-0.462). Similarly...