Take-home Exercise 1: Geospatial Analytics for Public Good

Setting the Scene

The locations of businesses and firms are central to urban development, influencing economic growth, job creation, and the availability of resources and services for residents and other businesses. Strategic locations provide competitive advantages, access to talent and infrastructure, and impact brand image and market positioning, while also shaping the city’s overall character and functionality by fostering specialized districts and facilitating efficient supply chains and employee commutes.

Studying the spatio-temporal patterns of businesses and firms is crucial for urban development because it reveals how economic activity and urban forms evolve, enabling better urban planning, promoting sustainability, identifying urban inefficiencies, and enhancing quality of life. By understanding where and when businesses locate and operate, urban planners can design cities more effectively, anticipate growth, ensure resource efficiency, and tailor infrastructure to support economic vitality and community well-being.

Objectives

In this take-home exercise, we are interested to investigate the spatial and spatio-temporal patterns of new businesses and firms established in Singapore for the first six months of 2025

The specific objectives of this take-home exercise are as follows:

  • To identify the “where” and “when” of new business entities, revealing clusters, patterns, and trends that are crucial for informed urban planning, resource allocation, and economic development.
  • To identify complex interaction patterns and emergent phenomena in the distribution of new businesses beyond simple proximity or density, such as clustering of related businesses, the influence of past trends on future locations, and the overall structural development of commercial areas, which are crucial for scientific urban planning and policy-making.

By understanding where businesses are located and how their distribution changes over time, urban planners nad managers can unlock spatial intelligence, optimize operations, and create effective strategies to attract investment and promote sustainable growth.

The Tasks

The specific tasks of this hands-on exercise are as follow:

  1. Download ACRA (Accounting and Corporate Regulatory Authority) Information on Corporate Entities datasets from data.gov.sg.

  2. Select at least three business types by referring to 2-digit or 3-digit Singapore Standard Industrial Classification (SSIS) 2020. Prepare and tidy the data by applying appropriate data wrangling method.

  3. Extract the selected business entities registered between 1st January 2024 to 30th June 2025.

  4. Geocode the selected records and convert them into simple point features.

  5. Compute spatial and spatio-temporal KDE of the selected business types.

  6. Perform second order spatial and spatio-temporal analysis on the selected business types.

  7. Describe and discuss the analysis results obtained from Step 5 and 6.

  8. For each geovisualisation prepared, write a short report of not more than 150 words describing the spatio-temporal patterns revealed by the geovisualisation and statistical graphics.

  9. With reference to the analysis results above, discuss how these findings can be used to support urban land use planning and management. (Not more than 500 words)

The Data

For the purpose of this exercise, Information on Corporate Entities datasets of ACRA must be used. There are a total of 27 csv files organised alphabetically. To extract business entities of interest, you need to refer to 2-digit or 3-digit Singapore Standard Industrial Classification (SSIS) 2020.

Students are free to include other data sets to support the analysis.

Grading Criteria

This exercise will be graded by using the following criteria:

  • Geospatial Data Wrangling (20 marks): This is an important aspect of geospatial analytics. You will be assessed on your ability to employ appropriate R functions from various R packages specifically designed for modern data science such as readr, tidyverse (tidyr, dplyr, ggplot2), sf just to mention a few of them, to perform the entire geospatial data wrangling processes, including. This is not limited to data import, data extraction, data cleaning and data transformation. Besides assessing your ability to use the R functions, this criterion also includes your ability to clean and derive appropriate variables to meet the analysis need.
Warning

All data are like vast grassland full of land mines. Your job is to clear those mines and not to step on them).

  • Geospatial Analysis (25 marks): In this exercise, you are expected to utilize the geospatial analytics methods introduced in class, along with the R packages provided during the hands-on exercises, to perform your analysis. You will be assessed on your ability to apply these methods correctly and to provide accurate interpretations and discussions of the analysis results.

  • Geovisualisation and Geocommunication (25 marks): In this section, your ability to effectively communicate complex geospatial analysis results through user-friendly visual representations will be assessed. Since this course is focused on geospatial analysis, it is crucial that you demonstrate proficiency in using appropriate geovisualization techniques to clearly convey the findings of your analysis.

  • Reproducibility (20 marks): This is a key learning outcome of this course. You will be assessed on your ability to thoroughly document the analysis procedures using code chunks within Quarto. It is important to note that simply providing the code chunks is insufficient; you must also include explanations of the purpose behind each step and the R function(s) used.

  • Bonus (10 marks): Demonstrate your ability to employ methods beyond what you had learned in class to gain insights from the data. The methods used must be geospatial in nature.

Submission Instructions

  • The write-up of the take-home exercise must be in Quarto html document format. You are required to publish the write-up on Netlify.
  • The R project of the take-home exercise must be pushed onto your Github repository.
  • You are required to provide the links to Netlify service of the take-home exercise write-up and github repository on eLearn.

Due Date

24th September 2025 (Wednesday), 11.59pm (midnight).

References

  • Park, S., Seo, H. & Koo, H. “Exploring the spatio-temporal clusters of closed restaurants after the COVID-19 outbreak in Seoul using relative risk surfaces”. Sci Rep 13, 13889 (2023). https://doi.org/10.1038/s41598-023-40937-5
  • Ben-Said, M. “Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review.” Ecol Process 10, 56 (2021). https://doi.org/10.1186/s13717-021-00314-4
  • Ding, X., Zheng, F., Lyu, G. et al. “An exploration of the spatial and temporal factors influencing industrial park vitality using multi-source geospatial data”. Sci Rep 15, 29584 (2025). https://doi.org/10.1038/s41598-025-15294-0

Peer Learning