GeoJson Layers

A custom GeoJSON layer delineates the routes of various MRT lines in Singapore, represented by polygons. Each line segment corresponds to a specific MRT line, such as the North East Line, East West Line, North South Line, Circle Line, Downtown Line, and Thomson East Coast Line. These polygons depict the geographic extent of each MRT line's route, facilitating spatial analysis and visualization of property transactions occurring near MRT stations.


SQL Query
An SQL query which calculates the average transacted price and average unit price per square foot (PSF) of properties within a 1000-meter radius of each MRT station was used. It first constructs a GeoPoint for each station using latitude and longitude coordinates. Then, it retrieves relevant data from another table, filtering by properties with non-null latitude and within the last 30 days. It categorizes properties as either Leasehold or Freehold based on their tenure. The query performs a left join based on the spatial relationship between the MRT station GeoPoint and the property GeoPoint. The results are grouped by station name, MRT ID, MRT line, station latitude, station longitude, and tenure type. This query enables analysis of property prices near MRT stations, crucial for understanding real estate trends and investment opportunities linked to Singapore's public transportation infrastructure. Additionally, the use of GeoPoints and spatial functions enhances the accuracy of data analysis by considering geographic proximity, a critical factor in property valuation and market dynamics.

Python Script to interface with Hex Frontend


 

This Python script processes and analyzes data related to MRT (Mass Rapid Transit) stations and real estate properties in Singapore. Here's a breakdown:

1. Data Preprocessing: The script begins by manipulating a DataFrame (dataframe_4) containing information about MRT stations and their associated properties. It handles missing values in the "Tenure" column, extracts the MRT line from the "mrt_id" column, and merges additional MRT line data into the DataFrame.

2. Geospatial Operations: It loads a GeoJSON file containing geographic information about MRT lines and merges this data with the DataFrame. Additionally, it seems to handle geometric data, possibly for mapping purposes.

3. User Input Handling: The script appears to interact with user input variables (NSL, EWL, NEL, CC, DTL, TEL, input_2) to filter the DataFrame based on MRT lines and tenure type.

4. Data Presentation: The script formats numerical data (average price and price per square foot) into currency format and prepares the DataFrame for presentation or further analysis.

5. Output: Finally, it seems to generate a subset of the DataFrame based on user-specified criteria and calculates maximum and minimum values from another DataFrame (dataframe_5). The script then formats this data for display, potentially in a user interface or report.

Overall, the script combines data manipulation, geospatial analysis, user interaction, and data formatting to provide insights into property prices near MRT stations in Singapore.