Existing JADETABEK EVCS

Instructions for Utilizing the Dataset

1. Loading the Dataset

Ensure you have a Python environment with necessary libraries (e.g., pandas, numpy) to load and process the dataset. Example code:

import pandas as pd

# Load the dataset

file_path = 'path_to_file.xlsx'

data = pd.read_excel(file_path)

2. Analyzing the Data

Use basic pandas functions to explore the data:

# View first few rows

data.head()

# Summary statistics

data.describe()

# Check for missing values

data.isnull().sum()

3. Performing Geospatial Analysis

The dataset includes geospatial coordinates (latitude, longitude, easting, northing). Use libraries like geopandas or folium for mapping and spatial analysis.

import folium

# Create a map centered on a location

m = folium.Map(location=[-6.2, 106.8], zoom_start=10)

# Add SPKLU locations

for _, row in data.iterrows():

    folium.Marker([row['latitude'], row['longitude']], popup=row['evcs_name']).add_to(m)

m.save('map.html')

4. Conducting Statistical Analysis

Perform analyses to identify patterns, trends, or correlations, such as:

• Analyzing usage patterns (num_transaction, sum_of_kwh)
• Examining spatial proximity and user density
• Evaluating charger type distributions

5. Visualization

Use libraries like matplotlib or seaborn for data visualization:

import matplotlib.pyplot as plt

import seaborn as sns

# Plot transaction distribution

sns.histplot(data['num_transaction'], bins=20)

plt.show()