GeViz

Team

Proposal

Poster

Application

Research Paper

PROBLEM & MOTIVATION

GeBIZ is a Singapore Government’s one-stop e-procurement portal which facilitates tender activities between Singapore government and local and overseas suppliers. Currently, there is no available tool to aid the public and ministries to understand and gain insights on the procurement made by the government under each ministry. Hence, we are motivated to create an interactive visualisation tool on government's procurement spending to allow the public and ministries to identify spending patterns and gain insights into procurement spending under each ministry.

OBJECTIVES

In this project, we are creating a visualisation that is able to show the following:

• Gain an overview of procurement spending made by each ministry and agency
• Identify the relationships between ministries, agencies and suppliers
• Identify what are the goods and services procured by ministries and agencies under each category

SELECTED DATASETS

The following datasets will be used for analysis , as elaborated below:

Dataset/Source	Data Attributes	Rationale of Usage
Government Procurement Data (https://data.gov.sg/dataset/government-procurement)	Tender No Agency Tender Description Award Date Tender Detail Status Supplier Name Awarded Amount	To gain information on government procurement such as tender description, amount and supplier information
Ministry and Agencies List	Ministry Agency	We will be looking through the Singapore Government Directory (https://www.gov.sg/sgdi/ministries) to categorise the agencies into their respective ministries. This will allow us to visualise the procurement spending on a ministry level.

Text Classification using Support Vector Classifier (SVC)

One of the key challenges of working with the provided procurement dataset is the absence of categorization of each procurement transaction. Instead of labelling manually, we applied machine learning to classify the tender descriptions into different categories. We firstly scraped the procurement descriptions and categories from GeBiz website using Selenium and BeautifulSoup libraries in Python to be used as the training and validation dataset in our Support Vector Classifier model. We were able to achieve 90% for training accuracy before performing the categorization prediction.

Government Procurement Dataset after Text Classification

Dataset/Source	Data Attributes
Government Procurement Data	Tender No Agency Tender Description Award Date Tender Detail Status Supplier Name Awarded Amount Category Sub Category

BACKGROUND SURVEY

Some of these visualizations that we draw inspiration from, are as follows: To be filled!

CONSIDERATION & VISUAL SELECTION

To be filled!

BRAINSTORMING SESSIONS

First Draft

[1] Treemap to show the spending breakdown for each category of all agencies under the selected ministry. The filters are year and ministry.
[2] Network diagram to show the relationship of agencies and suppliers of the selected ministry. The filters are year and ministry.
[3] Sankey diagram to show the cash flow between selected agency and suppliers for the selected category. The filters are year, ministry, agency and category [4] Word cloud to show an overview of the tender description for the selected agency and selected category. The filters are year, ministry, agency and category.

TECHNOLOGIES

Tools and technologies

Data Architecture

KEY CHALLENGES

The following are some of the key technical challenges that we may face throughout the course of the project:

Key Challenges	Mitigation Plan
Unfamiliarity with Javascript Rshiny libraries )	Attend R Shiny Workshop Independent learning via online resources Ask team mates for help
Unfamiliarity with R Libraries for Machine Learning and Selenium	Independent learning via online resources Clean, transform and analyse data together
Data Cleaning and Transformation	Need to crawl data on website to obtain company information Clean, transform the data together

TIMELINE

To be filled!

COMMENTS

Feel free to leave us some comments so that we can improve! We dont bite :)

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