Hong Kong Social Contact Dynamics by Kaggle | Demographics and Population Studies

About this Dataset

Hong Kong Social Contact Dynamics

Understanding Age, Gender and Network Dynamics

About this dataset

This dataset provides an in-depth look at the dynamics of social interaction, particularly in Hong Kong. It contains comprehensive information regarding individuals, households and interactions between individuals such as their ages, frequency and duration of contact, and genders. This data can be utilized to evaluate various social and economic trends, behaviors, as well as dynamics observed at different levels. For example, this data set is an ideal tool to recognize population-level trends such as age and gender diversification of contacts or investigate the structure of social networks in addition to the implications of contact patterns on health and economic outcomes. Additionally, it offers valuable insights into dissimilar groups of people including their permanent residence activities related to work or leisure by enabling one to understand their interactions along with contact dynamics within their respective populations. Ultimately this dataset is key for attaining a comprehensive understanding of social contact dynamics which are fundamental for grasping why these interactions are crucial in Hong Kong's society today

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How to use the dataset

This dataset provides detailed information about the social contact dynamics in Hong Kong. With this dataset, it is possible to gain a comprehensive understanding of the patterns of various forms of social contact - from permanent residence and work contacts to leisure contacts. This guide will provide an overview and guidelines on how to use this dataset for analysis.

Exploring Trends and Dynamics:

To begin exploring the trends and dynamics of social contact in Hong Kong, start by looking at demographic factors such as age, gender, ethnicity, and educational attainment associated with different types of contacts (permanent residence/work/leisure). Consider the frequency and duration of contacts within these segments to identify any potential differences between them. Additionally, look at how these factors interact with each other – observe which segments have higher levels of interaction with each other or if there are any differences between different population groups based on their demographic characteristics. This can be done through visualizations such as line graphs or bar charts which can illustrate trends across timeframes or population demographics more clearly than raw numbers would alone.

Investigating Social Networks:

The data collected through this dataset also allows for investigation into social networks – understanding who connects with who in both real-life interactions as well as through digital channels (if applicable). Focus on analyzing individual or family networks rather than larger groups in order to get a clearer picture without having too much complexity added into the analysis time. Analyze commonalities among individuals within a network even after controlling for certain factors that could affect interaction such as age or gender – utilize clustering techniques for this step if appropriate– then focus on comparing networks between individuals/families overall using graph theory methods such as length distributions (the average number of relationships one has) , degrees (the number of links connected from one individual or family unit), centrality measures(identifying individuals who serve an important role bridging two different parts fo he network) etc., These methods will help provide insights into varying structures between large groups rather than focusing only on small-scale personal connections among friends / colleagues / relatives which may not always offer accurate portrayals due to their naturally limited scope

Modeling Health Implications:

Finally, consider modeling health implications stemming from these observed patterns– particularly implications that may not be captured by simpler measures like count per contact hour (which does not differentiate based on intensity). Take into account aspects like viral transmission risk by analyzing secondary effects generated from contact events captured in the data – things like physical proximity when multiple people meet up together over multiple days

Research Ideas

Analyzing the age, gender and contact dynamics of different areas within Hong Kong to understand the local population trends and behavior.

Investigating the structure of social networks to study how patterns of contact vary among socio economic backgrounds.

Examining permanent residence, work and leisure contact patterns in order to understand the roles of individuals and identify potential risk factors for disease transmission within a population

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: 2017_Leung_HongKong_participant_extra.csv

Column name	Description
mode_survey	The type of survey used to collect the data. (String)
age_or_range	The age or age range of the individual. (Integer/String)
full_or_part_time	Whether the individual is employed full-time or part-time. (String)
work_role	The role of the individual in their job. (String)
edu	The level of education of the individual. (String)
indiv_or_family_income	The individual or family income of the individual. (Integer)
place_birth	The place of birth of the individual. (String)
district	The district in which the individual resides. (String)
ethnicity	The ethnicity of the individual. (String)
include_all_contacts	Whether all contacts were included in the survey. (Boolean)
num_left_out	The number of contacts left out of the survey. (Integer)

File: 2017_Leung_HongKong_sday.csv

Column name	Description
day	The day of the contact. (Integer)
month	The month of the contact. (Integer)
year	The year of the contact. (Integer)
dayofweek	The day of the week of the contact. (String)
decima_date	The date of the contact in Decima format. (String)
assign_date	The date of the contact in Assign format. (String)
actual_hour	The hour of the contact. (Integer)

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 2017 Leung Hongkong Contact Common

@kaggle.thedevastator_hong_kong_social_contact_dynamics.n_2017_leung_hongkong_contact_common

93.81 KB
8260 rows
15 columns


CREATE TABLE n_2017_leung_hongkong_contact_common (
  "part_id" BIGINT,
  "cont_id" BIGINT,
  "cnt_age_exact" DOUBLE,
  "cnt_age_est_min" VARCHAR,
  "cnt_age_est_max" VARCHAR,
  "cnt_gender" VARCHAR,
  "cnt_home" BOOLEAN,
  "cnt_work" BOOLEAN,
  "cnt_school" BOOLEAN,
  "cnt_transport" BOOLEAN,
  "cnt_leisure" VARCHAR,
  "cnt_otherplace" BOOLEAN,
  "frequency_multi" DOUBLE,
  "phys_contact" DOUBLE,
  "duration_multi" DOUBLE
);

N 2017 Leung Hongkong Contact Extra

@kaggle.thedevastator_hong_kong_social_contact_dynamics.n_2017_leung_hongkong_contact_extra

56.78 KB
8260 rows
3 columns


CREATE TABLE n_2017_leung_hongkong_contact_extra (
  "cont_id" BIGINT,
  "contact_age_or_range" BIGINT,
  "contact_member" DOUBLE
);

N 2017 Leung Hongkong Hh Common

@kaggle.thedevastator_hong_kong_social_contact_dynamics.n_2017_leung_hongkong_hh_common

13.97 KB
1149 rows
3 columns


CREATE TABLE n_2017_leung_hongkong_hh_common (
  "hh_id" VARCHAR,
  "country" VARCHAR,
  "hh_size" BIGINT
);

N 2017 Leung Hongkong Hh Extra

@kaggle.thedevastator_hong_kong_social_contact_dynamics.n_2017_leung_hongkong_hh_extra

48.38 KB
2995 rows
8 columns


CREATE TABLE n_2017_leung_hongkong_hh_extra (
  "hh_id" VARCHAR,
  "hh_member_id" VARCHAR,
  "residence_type" BIGINT,
  "num_usual_contacts" BIGINT,
  "usual_age_or_range" BIGINT,
  "usual_age_or_mid_range" DOUBLE,
  "usual_gender" BIGINT,
  "usual_relation" BIGINT
);

N 2017 Leung Hongkong Participant Common

@kaggle.thedevastator_hong_kong_social_contact_dynamics.n_2017_leung_hongkong_participant_common

23.45 KB
1149 rows
4 columns


CREATE TABLE n_2017_leung_hongkong_participant_common (
  "part_id" BIGINT,
  "hh_id" VARCHAR,
  "part_age" BIGINT,
  "part_gender" VARCHAR
);

N 2017 Leung Hongkong Participant Extra

@kaggle.thedevastator_hong_kong_social_contact_dynamics.n_2017_leung_hongkong_participant_extra

23.2 KB
1149 rows
14 columns


CREATE TABLE n_2017_leung_hongkong_participant_extra (
  "part_id" BIGINT,
  "mode_survey" BIGINT,
  "age_or_range" BIGINT,
  "full_or_part_time" BIGINT,
  "work_role" BIGINT,
  "edu" BIGINT,
  "indiv_or_family_income" BIGINT,
  "income_mid_range" BIGINT,
  "place_birth" BIGINT,
  "district" BIGINT,
  "ethnicity" BIGINT,
  "permanent_residence" BIGINT,
  "include_all_contacts" BIGINT,
  "num_left_out" DOUBLE
);

N 2017 Leung Hongkong Sday

@kaggle.thedevastator_hong_kong_social_contact_dynamics.n_2017_leung_hongkong_sday

31.69 KB
1149 rows
9 columns


CREATE TABLE n_2017_leung_hongkong_sday (
  "part_id" BIGINT,
  "sday_id" BIGINT,
  "day" BIGINT,
  "month" BIGINT,
  "year" BIGINT,
  "dayofweek" BIGINT,
  "decima_date" DOUBLE,
  "assign_date" BIGINT,
  "actual_hour" BIGINT
);