Alexander Fishkov, Ph.D. student Computer Science
Alexander is a Ph.D. student in Computer Science. He currently holds B.S. and M.S. degrees in Applied Math. He has experience working for industry major companies performing research in the fields of machine learning, data mining and natural language processing. In his free time, Alexander enjoys hiking, Nordic skiing and traveling.
In this post, we analyze migration data provided by the IRS. In an earlier post, we explored the nationwide characteristics of relocation. This time we focus on a lower level and study which states are the most popular relocation destinations for people across the U.S.
From the following map, we can see that most people relocate locally, usually to the neighboring states. The obvious exceptions are Alaska and Hawaii, but even for these states, the distribution of out-migrants is non-uniform.
Every year, millions of people in the U.S. relocate for various reasons: a job offer, family circumstances or simply striving for a change of scenery. To study relocation patterns we analyzed migration data provided by IRS. The data is based on taxpayers’ reports and is provided at the county and state level. In this post, we will focus on migration between states.
Throughout history, the U.S. has been seen as a place of tremendous opportunity and progress. Many people took the challenge to pursue a new life in this country, and many of them stayed. However, western society has not stayed the same — each generation was influenced by different economic and social events. In this post, we will explore how migration patterns changed with each new generation.
According to Dictionary.com, “generation” can be defined as “the entire body of individuals born and living at about the same time,” or “the term of years, roughly 30 among human beings, accepted as the average period between the birth of parents and the birth of their offspring.” Other definitions of the term include shared ideas and attitudes.
Following our post about marriage in same-sex couples, we will explore this topic a bit more. It hasn’t been long since June 26, 2015, when same-sex marriage became legal in all U.S. states. Many couples may not have registered their relationship yet. It is also not uncommon for opposite-sex couples to live together without registering their marriage. To aid in identifying such relationships, IPUMS USA introduced a specially-constructed variable to their version of the American Community Survey. Using answers from different questions of the survey form, they attempt to pinpoint the respondent’s potential spouse in the same household.
On June 26, 2015, The United States Supreme Court ruled in the famous Obergefell v. Hodges case that state-level bans on same-sex marriage are unconstitutional.
This decision effectively made same-sex marriage legal in all U.S. states and Washington, D.C., as well as all U.S. territories except for American Samoa, but not on all Indian lands. Prior to the decision, many states already recognized same-sex relationships but were not obligated to recognize marriages registered in other jurisdictions.
The American Community Survey began providing data on same-sex married couples in 2013. This data, however, is limited to the householder and his or her spouse. In today’s post, we will explore the ACS data from the Census Bureau spanning years 2013-2015, focusing on same-sex married couples.
In this post, we will investigate the demographic features of same-sex couples. We will once again use American Community Survey data, namely the ACS 2015 five-year sample processed by the IPUMS project. In this version, IPUMS USA has introduced a specially-constructed variable to their version of the American Community Survey. Using answers to different questions of the survey form, they attempt to pinpoint the location of the respondent’s potential spouse in the same household. This allows us to identify both same- and opposite-sex couples.
In this post, we will explore the data from the American Time Use Survey and attempt to put the numbers in perspective — using data for the years of 2003-2014 will show us changes in daily activities in the last decade.
The geographic properties of an area often dictate the type of businesses that can locate themselves there. For example, you can’t have an inland naval base. Farms and agriculture, in general, are less likely to prosper on rocky mountains than on sunny plains. But today, with all the technological advancements we have, many jobs in intellectual labor, including IT, finance, hi-tech manufacturing and other new industries seem less likely to depend on climatic features.
To summarize then climate features of a territory, we used the existing climate zones classification prepared by the U.S. Department of Energy. Each county is assigned to one of eight climate zones. Definitions of these zones depend not on temperatures, but rather on accumulated heat (the unit is called “degree-days”). Zone 1 has the warmest climate, while zone 8 has the coldest — in the U.S., only certain parts of Alaska belong to this climate zone. We present a map of the climate zones below; you can click to zoom in on a state of interest.
Including a number sequence or a year in your login or account name is quite popular — some websites even suggest it in case your desired username is already taken. Following our posts on the 10 million passwords dataset, we now explore different digit sequences that occur in passwords.