Using Census Data to Analyze Community Wildfire Damage
“How can we use census data to analyze the extent that a community has been damaged by wildfire?”
A single wildfire can destroy entire towns. Even if only minor damage occurs, then a mass migration away from the municipality. This numerical change will be recorded in census data, which means that we can Use Census Data to Analyze Community Wildfire Damage. The best way to do so would be to find the one-year estimates before and after the event and measure the difference.
“How can we apply computational methods to demography?”
Demography is a centuries-old field of study relying upon time-honed methods. However, with the advent of computers, more advanced statistical processing in the form of Computational Demography. Computational demography uses computer resources to understand populational change and characteristic development.
Climate-Induced Infrastructure Insurance Divestment
“Why are some insurance companies divesting from carbon-intensive projects?”
Major infrastructure projects often need insurance to ensure stakeholders that their money is safe. However, some of these activities such as new power plants or gas pipelines are making the planet less safe due to their carbon-intensive nature. As a result, some insurance companies such as Chubb are limiting the amount of non-sustainable projects that they invest in. This Climate-Induced Infrastructure Insurance Divestment is growing exponentially at both the grassroots and systematic level. If you would like to help take action, check out what 350 Colorado has to say.
How Accurate Weather Prediction can Help Grid Resiliency
“How can weather forecasts help stabilize the grid?”
Weather forecasts have become apart of our daily lives. But did you know that they can also help with grid resiliency? By being able to forecast the future, we can obtain how large our cooling loads will have to be. And with this knowledge, we can get ancillary reserves ready when the time comes. This way, Accurate Weather Prediction can Help Grid Resiliency!
“How can we analyze data when we can’t directly measure data?”
Scientists love to analyze data. However, sometimes we may not be able to directly obtain data. For example, when NASA studies the temperature near their rocket thrusters, they can’t place a sensor directly connected since the heat of the flame will burn it up. So instead, how about we indirectly look at this data through other means, such as the surrounding temperature of space and infer the engine’s temperature with interpolation. This technique is known as a Kalman FIlter and is used by scientists and engineers from all over the world.
“How can we determine if there is a relationship between two phenomena?”
One of the most fundamental aspects of science is trying to find a causal relationship between two factors. To do this, we can use a statistical technique called Linear Regression which calculates how closely knit the independent and dependent variables are.
Simple Moving Averages
“How can we make an average of constantly changing data?”
Data is all around us. And one often times we receive this data in real-time, whether it be in energy generation or the value of cryptocurrency. So how can we analyze any indicators of this data? Well, we can take a certain number of the most recent set of the data, such as the most last four data points, and simply take the average of that. This method is known as the Simple Moving Average. If the current data points are above or below the Simple Moving Average, then it will send a signal indicator.
“How can we select relevant features from data?”
Engineers and Scientists (both Natural and Social) analyze data to see what causes what. Often times, they will start with a set of factors that they think that is relevant, and trim down what does not have any effect. This is known as Feature Selection and is one of the underlying tools behind statistics and machine learning.
“How can we measure the difference between two sample populations?”
As Engineers and Social/Natural Scientists, we often like to measure the differences between sample populations. However, how can we know how far apart they truly are and whether or not these sample populations are representative of the entire population? Well, what if we were to measure the difference between their median values and then divide that by difference of their standard distributions? This is known as the T-Test and is used to quantify two population’s difference and its measurement accuracy.