Data Analysis Group 1
Here I wanted to explain/analyze the data I was able to get fro group 1.
The Data analysis was measured based on the slope of the interest lines of Tucson and Phoenix. The slope of the cities’ interest lines was compared to the temperature line. If the slopes were similar then this would show that there was a correlation between the temperatures of 2023 and the drought/water conservation interest. In this paper drought/water conservation interest will be defined as the search interest for all the terms used in this paper since theory is all related to the topic of drought and water conservation.
Overall the data from the graphs form a strong conclusion about the drought/water conservation interest in relation to the high temperatures of the summer of 2023. There is no correlation between the drought/water conservation interest and the temperature patterns of the year 2023 for the cities of Tucson and Phoenix. Additionally, although Tucson is more water-conservative than Phoenix, there were more zero-value data points for Tucson overall. However, does no correlation mean that Google Trends is an unreliable source? Actually, the lack of patterns and correlations between the slope lines show that there is a lack of interest in general in Phoenix and Tucson regardless of the state of weather in Arizona. Since the slope for all terms except for “drip irrigation” weren’t similar for both cities this shows a lack of care in general and that hotter temperatures won’t get Arizonians to become more interested in drought and water conservation.
lthough the graphs proved that there is no correlation between drought/water conservation interest and temperature, these findings have notable limitations. Firstly Google Trends does not give the exact number of searches for the terms. All of the numbers are relative from 0-100. This means that if the greatest amount of searches in a week was 5 people out of the entire year, the search interest would still show as 100 for that week. This scale skews the perspective of the graph even if the amount of people who searched the term was very low. Additionally, the data points were very sparse due to the location and terms that were used in this paper. For the terms, Aquifer, Water metering, Drip Irrigation, and Weed removal there were less non-zero values for both cities, especially Phoenix. This is because the terms are more specific and niche. While broader terms like Drought, Colorado River, and Groundwater had more of a slope that could be followed. This is because these were broader terms. Additionally, Tucson has more zero-valued data points. Based On the graph I suspect this has to do with the lower population in Tucson. Lastly, there were many terms that Google Trends could not generate a graph for. This limited how many graphs this paper could analyze.