What is Geospatial Data Analysis? It involves studying information tied to specific locations on Earth, like mapping where things are and understanding patterns in the data based on geography. It helps in making decisions related to things like urban planning, disaster response, and environmental management, enabling informed actions to address spatial challenges and improve resource allocation.
One can analyze geospatial data using software like ArcGIS, QGIS, GRASS GIS, Google Earth Engine, and R with packages like sf and rgdal. These tools offer a wide range of functionalities for mapping, spatial analysis, visualization, and modeling, catering to diverse needs in geospatial research and applications.
PROJECTS
1. Analyzing Vegetation Health and Socio-Economic Factors in Sacramento County
Objective: to examine the relationship between vegetation health, as indicated by NDVI (Normalized Difference Vegetation Index), and socio-economic factors, specifically household income, in Sacramento County, California.
Tools Utilized: R (dplyr, tidyverse, tidyr, sf, and tidycensus), ArcGIS Pro, and Microsoft Excel
The analysis begins by processing Landsat satellite imagery, combining the red (i.e the red band of Landsat satellite imagery covering the Sacramento County area) and near-infrared bands to create a raster multispectral image. NDVI, a common vegetation index, is then calculated from this multispectral image using ArcGIS Pro. NDVI values represent the density and health of vegetation in the area, with higher values indicating healthier vegetation.
Next, income information for Sacramento County is obtained from the 2019 5-year American Community Survey (ACS) using the tidycensus package in R. The percentage of households earning less than $35,000 per year in each census tract is calculated. Spatial data for census tracts is acquired from the Census Bureau's TIGER/Line website and joined with the income data obtained from tidycensus. This allows for the visualization of the proportion of low-income households in each census tract.
Using ArcGIS Pro, zonal statistics are performed to calculate statistics on NDVI values within each census tract. This step is crucial as it allows for the aggregation of NDVI information at the census tract level, providing a more localized understanding of vegetation health across Sacramento County. By obtaining the mean NDVI value for each tract, one can assess the overall vegetation health within different socio-economic contexts, aiding in the analysis of the relationship between vegetation health and household income.
Finally, the relationship between average NDVI values and the percentage of low-income households is analyzed using a scatter plot created in Excel. The analysis concludes that there is a low correlation between NDVI and household income in Sacramento County, as indicated by a low R-squared value of 0.1605. This suggests that NDVI is unlikely to be a dependent variable of household income or vice versa.
2. Analysis of Equatorial Guinea 
Objective: to analyze the region's slope, gross primary productivity (GPP), normalized burn ratio thermal (NBRT), and normalized difference vegetation index (NDVI).
Tools Utilized: Google Earth Engine (LandSat Image, MODIS, and NASA SRTM) and JavaScript
Definitions and color codes are available with code on github. Can this project be considered WebGIS also? 🤔