# All posts by Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics Andrey Kamenov is a data scientist working for Advameg Inc. His background includes teaching statistics, stochastic processes and financial mathematics in Moscow State University and working for a hedge fund. His academic interests range from statistical data analysis to optimal stopping theory. Andrey also enjoys his hobbies of photography, reading and powerlifting.

# Trends in motorcycle safety in the U.S.

Andrey Kamenov, Ph.D. Probability and Statistics

# Should you rent out your space on Airbnb year round?

Andrey Kamenov, Ph.D. Probability and Statistics

To anyone considering renting out their property on Airbnb, there is one extremely important question: How often will my property be rented? Sure, the daily rate may seem attractive for many, but if you don’t attract a lot of guests, the whole offer may not be near as lucrative.

On average, the occupancy rate (the percentage of time your property is rented — and makes you money) hovers at just below 50 percent in the U.S. This, of course, largely depends upon your pricing strategy as well as the property’s location.

Another important factor that may influence your decision to create a listing on Airbnb is whether you would have guests year round. In some cases, it may be a wiser choice to make your space available for just a few months each year.

# The number of pedestrian fatalities keeps growing, and here’s why

Andrey Kamenov, Ph.D. Probability and Statistics

The recently-released 2016 data on motor vehicle crashes from the NHTSA shows a grim fact: The number of pedestrian fatalities has reached an all-time high. It’s certainly worth it to take a look at possible reasons behind the growth in pedestrian fatalities — the FARS database contains rather detailed information about each crash.

First of all, a quick glance at the numbers reveals that the increase in fatalities can be narrowed down mostly to crashes that happened at night. In fact, daytime fatalities are roughly in line with the early 2000s numbers.

# Do SUVs really put other people at risk?

Andrey Kamenov, Ph.D. Probability and Statistics

A lot of news articles suggest that SUVs are inherently dangerous to both pedestrians and other drivers. This seems logical — these vehicles are usually heavier, so the energy of impact should be greater. In addition, SUVs are taller, which most likely puts pedestrians at greater risk in a possible crash.

Nevertheless, this 2005 report by the Insurance Institute for Highway Safety seems inconclusive about the possible dangers of utility vehicles. Well, maybe the more recent data will shed some light on this.

Andrey Kamenov, Ph.D. Probability and Statistics

Automatic braking is still considered a novelty in the car world. But the technology is maturing, causing many people to question if it’s going to be seen in more and more new cars each year. And can it really help significantly reduce the number of road fatalities?

Eleven percent of all fatal crashes in 2016 involved at least one driver who was distracted or drowsy — a record low figure (that means just under 3,800 crashes in absolute terms). We can consider this number a ballpark estimate for how many lives could have been saved by Automatic Emergency Braking (AEB).

# Small businesses’ share in government spending keeps rising

Andrey Kamenov, Ph.D. Probability and Statistics

The U.S. government has multiple different rules about awarding its contracts — one of these states that every agency should award a specific percentage of the total sum it spends to small businesses. For instance, the Department of Defence set its goal for small business prime contracting in 2018 at 22 percent. It also sets several other goals, including those for women-owned small businesses or small disadvantaged businesses.

Naturally, the goal differs from one agency to another, and the definition of small business itself changes depending on the industry.

On paper, the government’s efforts have been pretty effective. Small firms received 30.5 percent of the government’s total contract spending in 2017, up 1.8 percent from 2015. But these overall numbers don’t mean much to your small business since the percentage varies significantly between different industries and states, so let’s get a more detailed look. For example, it appears that small business’ participation numbers are markedly lower in the midwestern states.

# How much louder are big cities?

Andrey Kamenov, Ph.D. Probability and Statistics

Let’s have a look at the quietest and loudest cities in the U.S. To give you a broader perspective, only the top values are listed for each Census division. In order to compare the listed numbers, please keep in mind that a 10dBA difference is perceived as doubling the loudness. So the average background noise level of 50dBA (typical for big cities like Las Vegas or Los Angeles) can be described as four times the loudness of a quiet town (30dBA).

# As gas prices fall, light truck sales top cars in every state

Andrey Kamenov, Ph.D. Probability and Statistics

Vehicle sales in the U.S. bottomed out in 2009. The financial crisis hit light truck sales especially hard — the numbers here fell by more than 40 percent. In comparison, car sales saw a more modest 30 percent decrease. High gasoline prices in 2008 also certainly didn’t help in keeping sales afloat.

After a few years of steady recovery, the vehicle sales dynamic has been somewhat mixed in the recent years: You can observe this phenomenon in the chart below.

# Innovation in IT: businesses and trends

Andrey Kamenov, Ph.D. Probability and Statistics

What are the most innovative IT fields? The patent classification system can sometimes be hard to grasp. Additionally, the Patent and Trademark Office has recently finished the transition from one system to another. However, this doesn’t help us follow the hottest topics either. The mapping is available on the official website, but it is purely statistical. This means that you can’t really compare different classifications.

Another approach that we used was determining the topics ourselves. We based our method on applying the Latent Dirichlet Allocation algorithm to the (parsed and stemmed) application abstracts. Granted, this approach is relatively computationally expensive. The results are quite consistent with the original classification, but are, in fact, universal.

Here are the top five topics, based on the entire corpus of IT patent grants.

# Visualizing U.S. utility patents

Andrey Kamenov, Ph.D. Probability and Statistics

Exploring utility patents is no easy task. The system that is currently in place, Cooperative Patent Classification, has its issues. For example, many patent applications include multiple classifications only partially related to the invention itself. This proves useful if you are searching for something specific — on the other hand, it is not as helpful if you are interested in patent mining or visualization.

Luckily, one can easily accomplish most of these tasks with the help of advanced computational techniques. The most promising approach is the use of Natural Language Processing to classify and visualize patents based on their abstracts.

Let’s take a look at the algorithm known as Latent Dirichlet Allocation (or LDA for short). Its primary goal is to find the number of topics that are best suited for classifying a corpus of documents (patents, in our case).