Name: US Travel Check-Ins - Analysis
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

US Travel Check-Ins - Analysis

In-Depth Study of Location, Date, Temperature, USIndex, and Crime Rates

By [source]

About this dataset

This comprehensive dataset offers an in-depth exploration into US travel check-ins from Instagram. It includes detailed data scraped from Instagram, such as the location of each check-in, the USIndex for each state, average temperature for each state per month, and crime rate per state. In addition to location and time information, this dataset also provides latitude and longitude coordinates for every entry. This extensive collection of data is invaluable for those interested in studying various aspects of movement within the United States. With detailed insights on factors like climate conditions and economic health of a region at a given point in time, this dataset can help uncover fascinating trends regarding how travelers choose their destinations and how they experience their journeys around the country

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How to use the dataset

This Kaggle dataset - US Travel Check-Ins Analysis - provides valuable insights for travel researchers, marketers and businesses in the travel industry. It contains check-in location, USIndex rating (economic health of each state), average temperature, and crime rate per state. Latitude and longitude of each check-ins are also provided with added geographic context to help you visualize the data.

This guide will show you how to use this dataset for your research or business venture.

Step 1: Prepare your data
First and foremost, it is important to cleanse your data before you can analyze it. Depending on what sort of analysis needs to be conducted (e.g., time series analysis) you will need to select the applicable columns from the dataset that match your needs best and exclude any unnecessary columns such as dates or season related data points as they are not relevant here. Furthermore, variable formatting should be consistent across all instances in a variable/column category as well (elevation is a good example here). You can always double check that everything is formatted correctly by running a quick summary on selected columns using conditional queries like df['var'].describe() command in Python for descriptive results about an entire column’s statistical makeup including mean values, quartile ranges etc..

Step 2: Explore & Analyze Your Data Graphically
Once the data has been prepped properly you can start visualizing it in order to gain better insights into any trends or patterns that may be present within it when compared with other datasets or information sources simultaneously such as weather forecasts or nationwide trend indicators etc.. Grafana dashboards are feasible solutions when multiple dataset need to be compared but depending on what type of graphs/charts being used Excel worksheet formats can offer great customization options flexiblity along with various export file types (.csv; .jpegs; .pdfs). Plotting markers onto map applications like Google Maps API offers more geographical awareness that could useful when analyzing location dependent variables too which means we have one advantage over manual inspection tasks just by leveraging existing software applications alongside publicly available APIs!

Step 3: Interpretation & Hypothesis Testing
After generating informative graphical interpretation from exploratory visualizations the next step would involve testing out various hypotheses based on established correlations between different variables derived from overall quantitative estimates vizualizations regarding distribution trends across different regions tends towards geographical areas where certain logistical processes could yeild higher success ratios giving potential customers greater satisfaction than

Research Ideas

Travel trends analysis: Using this dataset, researchers could track which areas of the US are popular destinations based on travel check-ins and spot any interesting trends or correlations in terms of geography, seasonal changes, economic health or crime rates.

Predictive Modeling: By using various features from this dataset such as average temperature, US Index and crime rate, predictors could be developed to suggest how safe an area would feel to a tourist based on their current location and other predetermined variables they choose to input into the model.

Trip Planning Tool: The dataset can also be used to develop a tool that quickly allows travelers to plan trips according to their preferences in terms of duration and budget as well as preferred temperature range and easily identify areas with lower crime rates for maximum safety during travel

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: IG.csv

Column name	Description
Location	The geographic location of the check-in. (String)
Date	The date of the check-in. (Date)

File: USIndex.csv

Column name	Description
date	The date of the check-in. (Date)
Month	The month of the check-in. (String)
Year	The year of the check-in. (Integer)
Season	The season of the check-in. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Ig

@kaggle.thedevastator_us_travel_check_ins_analysis.ig

979.55 kB
70,114 rows
2 columns

CREATE TABLE ig (
  "location" VARCHAR,
  "date" VARCHAR
);

Ig Clean

@kaggle.thedevastator_us_travel_check_ins_analysis.ig_clean

326.13 kB
48,778 rows
4 columns

CREATE TABLE ig_clean (
  "location" VARCHAR,
  "month" BIGINT,
  "year" BIGINT,
  "season" VARCHAR
);

Location

@kaggle.thedevastator_us_travel_check_ins_analysis.location

1.01 MB
41,686 rows
7 columns

CREATE TABLE location (
  "location" VARCHAR,
  "month" BIGINT,
  "year" BIGINT,
  "season" VARCHAR,
  "address" VARCHAR,
  "latitude" DOUBLE,
  "longitude" DOUBLE
);

State Abbrevs

@kaggle.thedevastator_us_travel_check_ins_analysis.state_abbrevs

2.88 kB
51 rows
2 columns

CREATE TABLE state_abbrevs (
  "state" VARCHAR,
  "abbreviation" VARCHAR
);

Temperature

@kaggle.thedevastator_us_travel_check_ins_analysis.temperature

13.88 kB
1,813 rows
7 columns

CREATE TABLE temperature (
  "state" VARCHAR,
  "date" BIGINT,
  "value" DOUBLE,
  "anomaly" DOUBLE,
  "month" BIGINT,
  "year" BIGINT,
  "season" VARCHAR
);

Uscrime

@kaggle.thedevastator_us_travel_check_ins_analysis.uscrime

19.93 kB
156 rows
13 columns

CREATE TABLE uscrime (
  "year" BIGINT,
  "state" VARCHAR,
  "population" BIGINT,
  "violent_crime" BIGINT,
  "homicide" BIGINT,
  "rape_revised" DOUBLE,
  "robbery" BIGINT,
  "aggravated_assault" BIGINT,
  "property_crime" BIGINT,
  "burglary" BIGINT,
  "larceny" BIGINT,
  "motor_vehicle_theft" BIGINT,
  "total" DOUBLE
);

Usindex

@kaggle.thedevastator_us_travel_check_ins_analysis.usindex

4.89 kB
45 rows
5 columns

CREATE TABLE usindex (
  "date" TIMESTAMP,
  "n__value" DOUBLE  -- Value,
  "month" BIGINT,
  "year" BIGINT,
  "season" VARCHAR
);