#  Infogroup US Historical Business Dataset Analysis 

 



 This project involved creating geospatial measures for ~2,000 public firms from the Infogroup US Historical Business Dataset. One of the tasks involved calculating the following variables at the census block group level from the dataset for 23 years of data (1997 – 2019).

 1. Businesses per office size type (Office\_Size\_Code)  
2\. Businesses per sales volume (Location\_Sales\_Volume\_Code)  
3\. Businesses per employee size(Location\_Employee\_Size\_Code)  
4\. Businesses per Business\_Status\_Code  
5\. Number of establishments. Will be calculated from Year\_Established field.  
a. 1 year old  
b. 2 years old  
c. 3 years old  
d. 4 years old  
e. 5 years old  
f. 6-10 years old  
g. 11 and older  
6\. Total, average, median, top 90, lower 10 percentiles, min, max Employee\_Size\_Location  
7\. Total, average, median, top 90, lower 10 percentiles, min, max Sales\_Volume\_Location  
8\. Businesses per IDCode  
9\. Businesses per NAICS  
10\. Businesses per SIC  
11\. Total, average, median, top 90, lower 10 percentiles, min , max SALESVOL  
12\. Businesses per HDBRCH  
13\. Businesses per EMPNUM  
14\. Businesses per SQFTCODE  
15\. Census block group

 The project was implemented using [Pandas](https://pandas.pydata.org/) in Python on [Harvard's High Performace Computing Cluster.](https://www.rc.fas.harvard.edu/) A customised [Jupyter notebook](https://jupyter.org/) was designed for processing each year of dataset and can be easily used for other projects with this dataset.

 The scripts are can be found on our Github [here](https://github.com/cga-harvard/Data_Science_Big_Data_Projects/tree/master/scripts/Infogroup_scripts).

 Questions/comments on the project can be send to [Devika Kakkar](/people/devika-kakkar) and [Jeff Blossom](/people/jeff-blossom).



 



 

 See also:- [ Projects ](/research/project)
- [ big data ](/tags/big-data)
- [ High Performance Computing Cluster ](/tags/high-performance-computing-cluster)
- [ Python ](/tags/python)
- [ Data Science ](/tags/data-science)
- [ Pandas ](/tags/pandas)