Introduction
Welcome to our latest blog post! We're excited to share a new project idea with you called "Analyzing Public WiFi Hotspots in New York City: A Clustering Approach." In this project, we'll be diving into the world of WiFi hotspots across New York City. Our goal is to use smart techniques to find out if there are any patterns or clusters in where these WiFi spots are located. It's like trying to figure out if certain areas have more WiFi than others, and why that might be.
Project Requirements :
CONTEXT
As the demand and usage of the internet is growing, public wifi is becoming a new normal across the world. Even Indian government has decided to provide public wifi at certain locations. Wifi providers want to optimise the cost while providing the free and paid wifi. Here, we have records of public WiFi hotspots in New York City. The dataset consists of records for every public WiFi hotspot (ones provided by or in partnership with the city) in New York City. We would analyse the data and try to infer if the free public WiFi tends to cluster around certain (more affluent) areas.
DATA DESCRIPTION:
Feature | Description |
ObjectID | Identification number automatically generated by map software ArcMap. |
BORO | Borough of New York City. |
MN, BX, BK, QU, SI | Abbreviations for Manhattan, Bronx, Brooklyn, Queens, and Staten Island respectively. |
TYPE | Type of WiFi provided by franchise. |
PROVIDER | Franchise who is providing the Wifi connection. |
LAT | Latitude: Points that fall North or South of the Equator, expressed in degrees. |
LON | Longitude: Points that fall East or West of the Prime Meridian, expressed in degrees. |
LOCATION_T | The type of location that a WiFi hotspot is present in. |
CITY | The city in which a hotspot is located. |
BoroCode | The New York City borough where the hotspots are located. |
NTACode | Neighborhood Tab Access by number. |
OBJECTIVE:
To use Unsupervised Learning techniques such as clustering in order to identify clusters of wifi providers in the specific locations.
STEPS AND TASKS:
Exploratory Data Analysis(EDA):
A. Import and Read ‘wifi_data.csv’.
Check size of the dataset and write your observations about the features and their datatype
Convert/Encode required string features into numerical datatype
Select relevant features for model building and drop redundant or irrelevant features.
Univariate and Bivariate Analysis:
Which provider has highest number of wifi hotspots?
Which provider has highest number of free wifi hotspots?
List down the name of providers that provide “Limited Free” type of wifi.
What is the correlation coefficient between borough and BoroCode.
Are there any indoor free wifi hotspots?
Which city has the “partner_site” type wifi hotspot?
Clustering:
Apply following techniques to identify the natural cluster of wifi hotspots locations. And mention the number of optimal clusters and their
properties.
Normalize the data.
Build a K-means clustering model for k=2 and k=3.
Visualize the clusters.
Assign cluster labels to the dataset and perform bivariate analysis between cluster labels and various features and write your inferences.
Solution Approach
In this project, we aimed to analyze public WiFi hotspots in New York City using various data analysis and machine learning techniques. Here's a breakdown of our methodology:
Data Exploration and Preparation:
We started by loading the dataset containing information about WiFi providers in different boroughs of New York City.
Checked the size of the dataset and its data types.
Encoded categorical columns into numerical values using LabelEncoder.
Removed irrelevant or redundant features to streamline our analysis.
Univariate and Bivariate Analysis:
Identified the provider with the highest number of WiFi hotspots and the highest number of free WiFi hotspots.
Listed down providers offering "Limited Free" WiFi.
Conducted chi-square test to analyze the relationship between Borough and BoroCode.
Examined the presence of indoor free WiFi hotspots.
Identified the city with "partner_site" type WiFi hotspots.
Clustering:
Normalized the data using StandardScaler.
Built K-means clustering models for k=2 and k=3 to identify natural clusters of WiFi hotspots.
Visualized the clusters on scatter plots to understand their distribution.
Performed bivariate analysis between cluster labels and various features to derive insights.
Key Findings:
Significant relationship observed between Borough and BoroCode.
Clustering successfully separated WiFi hotspots based on geographic location.
No indoor free WiFi hotspots were found.
Affluence of the area did not determine the presence of free public WiFi hotspots.
Output
At CodersArts, we're embarking on a journey to shed light on the intricate network of public WiFi hotspots in New York City through our project, "Analyzing Public WiFi Hotspots in New York City: A Clustering Approach." With a keen focus on leveraging innovative clustering techniques, we aim to unravel patterns and clusters within the vast expanse of WiFi accessibility across the city. Our dedicated team is committed to delving deep into the digital connectivity landscape, providing valuable insights into the distribution and concentration of WiFi hotspots throughout different neighborhoods and boroughs.
From conceptualization to execution, CodersArts is your trusted partner every step of the way. We specialize in crafting sophisticated database systems tailored specifically to the demands of analyzing WiFi hotspots in urban environments. Utilizing advanced clustering algorithms and data analysis methodologies, we strive to empower organizations with the tools and insights necessary to optimize WiFi deployment strategies and enhance connectivity accessibility for residents and visitors alike.
But our mission extends beyond mere data analysis. CodersArts is dedicated to driving tangible outcomes in urban connectivity through actionable insights derived from our comprehensive analysis. By harnessing the power of advanced data analytics and visualization techniques, we aim to equip stakeholders with the knowledge and strategies needed to bridge the digital divide, foster community engagement, and promote equitable access to WiFi resources across New York City's diverse landscape. With CodersArts at your side, navigating the complexities of urban WiFi analysis has never been more straightforward.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.