{"id":10779,"date":"2018-10-10T01:15:00","date_gmt":"2018-10-10T05:15:00","guid":{"rendered":"http:\/\/studentwork.prattsi.org\/infovis\/?p=10779"},"modified":"2019-01-11T00:14:10","modified_gmt":"2019-01-11T05:14:10","slug":"10779","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/10779\/","title":{"rendered":"Job Industry As Per The Empire State Development Regions of NY From 2012 to 2017"},"content":{"rendered":"

Introduction<\/strong><\/h3>\n

The Great Recession without a doubt affected the country\u2019s economy, but the state of New York certainly did snap back. It is being said that the state is undergoing its largest and longest job expansion since World War II and New York City has been the driving force behind the employment growth in this particular state. A\u00a0new report\u00a0from state Comptroller Thomas DiNapoli found that since 2009, the city has experienced its most robust growth in modern history. In December 2017 the unemployment rate was just 4.3%, down from 10.2% in late 2009.<\/span><\/p>\n

As a budding designer who wants to work somewhere around New York City, this report grabbed my attention. While doing an in-depth research on this report, I came across an open data set that was published by Open Data NY. It talked about the growth in the job industry as per the Empire State Development Regions of New York (Figure1).\u00a0<\/span>In order to do a full analysis of the data set, I used Tableau Public<\/span>. <\/span><\/p>\n

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Figure1 – Regions of New York State divided as per the\u00a0Empire State Development Corporation<\/p>\n

Some of the information that I wanted to explore from the dataset was:<\/span><\/p>\n

    \n
  1. Which region has the most number of jobs for the quaternary sector (the sector that I wanted to explore the most)?<\/span><\/li>\n
  2. Which sector has the least number of jobs in the state of New York?<\/span><\/li>\n
  3. Jobs in which sector have dipped the most from 2012 to 2017?<\/span><\/li>\n<\/ol>\n

    <\/h3>\n

    Inspiration\/Critique<\/b><\/span><\/h3>\n

    While doing my research on the job industry in the state of New York, I came across a couple of visualizations that helped in making my visualizations better and understandable. Some of the examples that I liked:<\/span><\/p>\n

      \n
    1. <\/b> Employment Growth by Boroughs of\u00a0New York City<\/b><\/span><\/li>\n<\/ol>\n

      \"\"<\/p>\n

      Source –\u00a0https:\/\/osc.state.ny.us\/osdc\/rpt10-2018.pdf<\/a><\/p>\n

      This visualization represents the employment growth in 5 boroughs of New York City – Manhattan, Bronx, Brooklyn, Staten Island, and Queens. What I liked the most about this visualization was the map representation. It is easily understandable and looks clean from the design point of view. One thing that I think is missing from this visualization is the bifurcation of jobs in each borough. It gives the reader an overall idea of the job industry in a particular borough but it fails to inform them about a particular industry.<\/span><\/p>\n

       <\/p>\n

      2.\u00a0New York City Job Gains or Losses by Sector (2017)<\/b><\/p>\n

      \"\"<\/p>\n

      Source –\u00a0https:\/\/osc.state.ny.us\/osdc\/rpt10-2018.pdf<\/a><\/p>\n

      This visualization informs about the job gains and losses in 2017 by sector in New York City. According to me, this visualization does a good job in bifurcating jobs to different sectors. It gives the readers a clear idea about various job industries. Talking about the Y-axis of this visualization (Thousands of Jobs), I think as a reader I would be more interested in seeing the percentage increase\/decrease from the previous year or getting the exact job numbers, rather than getting an estimated number of jobs which we get from this bar graph visualization. That will give me a better idea of the job industry.<\/span><\/p>\n

       <\/p>\n

      Materials<\/b><\/span><\/h3>\n
        \n
      1. <\/b>Jobs by Industry in the Empire State Development Regions of New York From 2012 to 2017<\/b>:<\/a> The data set used for the analysis. This data set was published by Open Data NY.<\/span><\/li>\n
      2. <\/b>Tableau Public: <\/b><\/a>A free software that can allow anyone to connect to a spreadsheet or file and create interactive data visualizations for the web.<\/span><\/li>\n
      3. <\/b>OpenRefine:<\/b><\/a> A tool for working with messy data: cleaning it; transforming it from one format into another, and extending it with web services and external data.<\/span><\/li>\n<\/ol>\n

         <\/p>\n

        Method to Create This Visualization<\/b><\/span><\/h3>\n
          \n
        1. <\/b>Selecting the Data Set<\/b><\/span><\/li>\n<\/ol>\n

          This was to most difficult part of this project. With so much free data available online, I was really getting confused with the selection of the topic. Once I made my mind with the topic for the data it was easy to find the right data set as the websites with free datasets are easy to navigate and have good filter options to filter out content. The datasets were available in many formats but I chose to download a CSV file of it.<\/span><\/p>\n

          2. Refining the Data<\/b><\/span><\/p>\n

          After I download a CSV file of the data, I analyzed that there was some messy data within that data set which needed to be cleaned. So I decided to refine the data using Google\u2019s OpenRefine.\u00a0<\/span>Firstly I removed the column –\u00a0 <\/span>\u201cNAICS Code\u201d from my raw data because I already had the industry names in the data so the code column was not useful for my visualization. Then I applied a text facet to the industry column and found that there was a formatting issue like extra spacing with some data. After solving that spacing problem of the data, my data set was ready for the next step.<\/span><\/p>\n

          3. Data Visualization Using Tableau Public<\/b><\/span><\/p>\n

          After the refining phase, it was now time to import the refined file in tableau public. It was my first interaction with this software so it took me a while to get used to the interface of the software. I had to use a lot of hit and trial method to explore different features and options within the software. After exploring the software, I realized that I should combine some of the industries because showcasing 25 – 30 industries on one graph will make it difficult for the readers to understand the visualization. So I decided to combine the industries into 4 sectors – Primary, Secondary, Tertiary and Quaternary.<\/span><\/p>\n

          List of industries under each sector – <\/span><\/p>\n