Unveiling the Dance between Population Density and Carbon Footprint Levels using Bivariate Map
Published:
During my investigation of carbon footprint data for a personal research project, I became intrigued by its correlation with population proportions. To explore this connection, I initiated the process by plotting both variables (carbon footprint vs population density) on bivariate maps.
Upon reviewing the maps, it seems that there isn’t a consistent linear relationship (either positive or negative) between the variables. However, a notable spatial autocorrelation was apparent, indicating a systematic spatial distribution that goes beyond randomness.
It’s crucial to note that this observation is based solely on visualization, and further validation through statistics testing is advisable.
- Full project: https://github.com/ofitrahramadhan/bivariatemap_R
- Data Source: www.carbon.place
Housing has been an undeniable primary aspect of human life. Without a proper shelter, a person cannot fulfill his/her basic need in life. This need has given more pressure when we know that there is only a limited amount of land that can be utilized for it. On other side, the competition to obtain a proper housing is getting more intense overtime due to urbanization (more people are coming to urban area to settle in). That is the exact reason why the price for this resource is skyrocketed to the point of no return.
After learning about Google Earth Engine (GEE) from CASA0025: Building Spatial Applications with Big Data in 2024 from CASA, I was impressed by the vast potential of this tool. Not only does it provide access to extensive planetary data, but it also includes essential tools for in-depth analysis and prediction, which can be dynamically visualized and presented through its spatial application. It’s a truly full-stack process!