UK Social Contact Network by Kaggle | Demographics and Population Studies

About this Dataset

UK Social Contact Network

Age, Gender, and Household Characteristics

About this dataset

This dataset contains a detailed overview of the 2013 Van Hoek Social Contact Network Study in the UK. With this dataset, we have a unique opportunity to see how age, gender, and household size impact our social contact networks. From looking at the day of week contact was made and how often family contacted each other, to understanding the socio-economic backgrounds of participants and ethnicities represented - this data provides us with an interesting look into how our social connections are shaped. By diving deeper into these variables, we can gain valuable insight into our current culture's trends regarding who we interact with on a daily basis

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How to use the dataset

In order to use this dataset effectively, it is important to familiarize oneself with all of the columns included: dayofweek (the day of week on which contact was made), Contact.Day.if.different.from.allocated.day (the day of week on which contact was made if different from allocated day), day (day of month when contact was made), month (month when Contact was made), FTM (frequency of contact with family members), Socia_economic (social economic status participant belongs to), Noe_of _siblings (number of siblings participant has) ,Ethnicity(ethnicity details) part_age_detail(age detail).

Once you become familiar with all columns included in this dataset you can begin to identify relationships between these demographic factors and how different social contacts were enacted among them by examining how frequently households interacted as well as what age/gender/ethnic composition within each household look like at different times during a given month or year period; or even see what variables have an influence over who is contacted more often than not within a household setting or across multiple households- all depending on your need for specific insights from your research perspective!

Research Ideas

Analyzing seasonal Social Contact trends: Using the month and dayofweek features, we could analyse how contact tends to vary across different seasons (e.g., more contact during summer months).

Predicting Participants' Age Group: With the age detail, gender, and soci_economic features, predictive models can be built in order to estimate the age range of participants from given socio-demographic information.

Evaluating Cyber Bullying and Online Social Networking Trends: Studying the self-reported frequency of contact between family members (FTM), researchers can evaluate cyber bullying trends in various communities as well as measure changes in social network size over time with respect to a given demographic group such as gender or ethnicity

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: 2013_VanHoek_UK_sday.csv

Column name	Description
dayofweek	The primary day contact was made. (String)
Contact.Day.if.different.from.allocated.day	Any days when contact deviated from allocated days. (String)
day	The day of the month contact was made. (Integer)
year	The year contact was made. (Integer)

File: 2013_VanHoek_UK_participant_extra.csv

Column name	Description
FTM	Frequency of contact with family members. (Numeric)
Socia_economic	Socioeconomic status of each participant. (Categorical)
No.of.siblings	Number of siblings for each participant. (Numeric)
Ethnicity	Ethnicity for each participant. (Categorical)
part.age.detail	Age cohort associated with participants. (Categorical)

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

N 2013 Vanhoek Uk Contact Common

@kaggle.thedevastator_uk_social_contact_network.n_2013_vanhoek_uk_contact_common

17.45 KB
761 rows
15 columns


CREATE TABLE n_2013_vanhoek_uk_contact_common (
  "part_id" BIGINT,
  "cont_id" BIGINT,
  "cnt_age_exact" DOUBLE,
  "cnt_age_est_min" DOUBLE,
  "cnt_age_est_max" DOUBLE,
  "cnt_gender" VARCHAR,
  "cnt_home" BOOLEAN,
  "cnt_work" BOOLEAN,
  "cnt_school" BOOLEAN,
  "cnt_transport" BOOLEAN,
  "cnt_leisure" BOOLEAN,
  "cnt_otherplace" BOOLEAN,
  "frequency_multi" DOUBLE,
  "phys_contact" DOUBLE,
  "duration_multi" DOUBLE
);

N 2013 Vanhoek Uk Contact Extra

@kaggle.thedevastator_uk_social_contact_network.n_2013_vanhoek_uk_contact_extra

10.32 KB
761 rows
6 columns


CREATE TABLE n_2013_vanhoek_uk_contact_extra (
  "part_id" BIGINT,
  "cont_id" BIGINT,
  "household_member" VARCHAR,
  "hold_5_min" VARCHAR,
  "miles_travelled" DOUBLE,
  "nursery_childcare" BIGINT
);

N 2013 Vanhoek Uk Hh Common

@kaggle.thedevastator_uk_social_contact_network.n_2013_vanhoek_uk_hh_common

3.23 KB
116 rows
3 columns


CREATE TABLE n_2013_vanhoek_uk_hh_common (
  "hh_id" VARCHAR,
  "country" VARCHAR,
  "hh_size" DOUBLE
);

N 2013 Vanhoek Uk Hh Extra

@kaggle.thedevastator_uk_social_contact_network.n_2013_vanhoek_uk_hh_extra

18.06 KB
116 rows
25 columns


CREATE TABLE n_2013_vanhoek_uk_hh_extra (
  "hh_id" VARCHAR,
  "hh_relationship_1" VARCHAR,
  "hh_age_1" DOUBLE,
  "hh_sex_1" VARCHAR,
  "hh_relationship_2" VARCHAR,
  "hh_age_2" DOUBLE,
  "hh_sex_2" VARCHAR,
  "hh_relationship_3" VARCHAR,
  "hh_age_3" DOUBLE,
  "hh_sex_3" VARCHAR,
  "hh_relationship_4" VARCHAR,
  "hh_age_4" DOUBLE,
  "hh_sex_4" VARCHAR,
  "hh_relationship_5" VARCHAR,
  "hh_age_5" DOUBLE,
  "hh_sex_5" VARCHAR,
  "hh_relationship_6" VARCHAR,
  "hh_age_6" DOUBLE,
  "hh_sex_6" VARCHAR,
  "hh_relationship_7" VARCHAR,
  "hh_age_7" DOUBLE,
  "hh_sex_7" VARCHAR,
  "hh_relationship_8" VARCHAR,
  "hh_age_8" VARCHAR,
  "hh_sex_8" VARCHAR
);

N 2013 Vanhoek Uk Participant Common

@kaggle.thedevastator_uk_social_contact_network.n_2013_vanhoek_uk_participant_common

4.41 KB
116 rows
4 columns


CREATE TABLE n_2013_vanhoek_uk_participant_common (
  "part_id" BIGINT,
  "hh_id" VARCHAR,
  "part_age" DOUBLE,
  "part_gender" VARCHAR
);

N 2013 Vanhoek Uk Participant Extra

@kaggle.thedevastator_uk_social_contact_network.n_2013_vanhoek_uk_participant_extra

5.57 KB
116 rows
6 columns


CREATE TABLE n_2013_vanhoek_uk_participant_extra (
  "part_id" BIGINT,
  "ftm" VARCHAR,
  "socia_economic" VARCHAR,
  "no_of_siblings" DOUBLE,
  "ethnicity" VARCHAR,
  "part_age_detail" VARCHAR
);

N 2013 Vanhoek Uk Sday

@kaggle.thedevastator_uk_social_contact_network.n_2013_vanhoek_uk_sday

5.86 KB
116 rows
7 columns


CREATE TABLE n_2013_vanhoek_uk_sday (
  "part_id" BIGINT,
  "dayofweek" BIGINT,
  "contact_day_if_different_from_allocated_day" VARCHAR,
  "sday_id" VARCHAR,
  "day" VARCHAR,
  "month" VARCHAR,
  "year" BIGINT
);