Thailand Province Border Adjacency Dataset/Code

A quick update post to help get my latest project’s new dataset more readily indexed on Google search, etc. (Feb 8th 2021)

I’ve recently been working on risk assessment for COVID-19 in our 2nd wave. To create an email alert per province (taking account of local regional data) I needed to join provincial data together. It turns out that for much of Thailand’s publicly available government datasets (particularly in Office of Agricultural Economics, Land Department, etc) the data is summarised at Province level (i.e. is not GIS coordinate-based). Yet, there’s no mapping of province -> [neighbouring provinces] dataset out there (that I could find), so I created one the other night and wrote the code to verify and integrate it.

That dataset/code is now on github: https://github.com/pmdscully/thailand_province_border_adjacency

An obligatory requirement of using data relations (X->Y) is making a pretty visualisation on GraphViz, so dutifully — here it is: ^^ (Along with Wikipedia’s provincial public map for comparison..)

Q & A

Is it correct & up to date? Yes. The newest Thai province change was adding Bueng Kan, which was split-off from Nong Khai, effective on 23 March 2011 – that’s included; so it’s up-to-date as of Feb 2021. Bangkok is referred to as a Special Administrative Area, but it’s included as province in the mappings; giving a total of 77 entries.

Is it easy to use the mapping dataset by importing a Python module into my own software application? Yes, you can join province datasets together based on their semantic geo-neighbourhoods – 🙂

  1. Just git clone the repository,
  2. download a province naming dataset ,
  3. import the python module,
  4. Write about 4 lines of code gives you a dictionary lookup (see the readme.md for full details).

I want to SQL join my provincial datasets together, but only for the provinces nextdoor, how can I do that? Yes, that’s precisely what this dataset and code is for. Before you create your SQL query,

  1. import the Python module (province_neighbours.py),
  2. instantiate the ProvinceRelationsParser object,
  3. get the dictionary,
  4. perform the dictionary lookup on your key province, this will give you the list of neighbouring provinces.
  5. Simply plug those names into your SQL query and you are ready! (Find a code example in the readme.md).

Can I use Thai language (UTF-8) as my lookup and get neighbour results in Thai (UTF-8)? Short answer is yes. See the readme.md on the Github repo for full details with code samples.

Over to you

There’s plenty more to say about this project, but if you’re interested in the details, go visit the Github repository. (Or send me a message, if you want extra detailed info).

Feel free to check it out.

ON MEASURING MACHINE LEARNING MODELS AGAINST CONCRETE BUSINESS OBJECTIVES

REVIEW NOTES: DATA SCIENCE FOR BUSINESS BY PROVOST & FAWCETT: CHAPTER 7

I enjoyed reading this chapter. It’s insightful and well explained with detailed examples, diagrams and graphics, on a few data science topics that correspond directly to conventional scientific research in computer science. That makes me happy, because these are crucial points, yet rarely are the focus of Kaggle Competitions, books on Machine Learning or Statistics, the latest and greatest in TensorFlow, PyTorch, AutoML libraries (etc, etc) and too infrequently discussed in DL/AI/ML social posts and blogs. Below I have written about the points that are well worth taking home. These topics are broadly on:

  • Careful consideration of what is desired from data science results.
  • Expected value as a key evaluation framework.
  • Consideration of appropriate comparative baselines, in machine learning models.
Continue reading “ON MEASURING MACHINE LEARNING MODELS AGAINST CONCRETE BUSINESS OBJECTIVES”

Open Source Code for Light Stage Capture Sequences

Today I’m posting updates (1/n) to the Light Stage open source project codebase.

The updates mark improvements for integrating experimental result data and 3d geometry data with light and camera-trigger hardware controllers (3). Included are two new lighting sequence improvements (1) and (2) and a way to get started, no matter your stage design and target capture application (4). These changes contribute towards standardised capture sequences and integrated 3d reconstruction pipeline processing, while supporting stage design tools and retaining visualisations, measurable evaluations and optimisations at each step.

Altogether, this work takes a step towards the vision of a comprehensive open source framework for open hardware light stages, find more details at the Build a Light Stage website.

These recent updates to the LightStage-Repo on github include:

  1. Spherical gradient” lighting sequence.
  2. Balanced lighting baseline”.
  3. Local web service (on port 8080) to return data requested by an HTTP client, such as a hardware controller with Ethernet/Wifi module.
  4. Configuration file designed for each Light Stage, to easily get the web service responding with correct sequence data.
Continue reading “Open Source Code for Light Stage Capture Sequences”

Update on Lightstage Project

In this post, I took the liberty to write some of my thoughts and reflections on why Lightstages are (“pretty cool in my book” and also) relevant amongst today’s cutting edge developments in machine learning and data-driven decision making.

Over the last few years, I’ve had the opportunity to work as a researcher on the Aber Lightstage project, under Dr. Hannah Dee. Back then, I wrote a Python-OpenGL-based application to help us visualise and numerically evaluate lighting positions on our stage — the project is open source and on Github. Dr. Dee had successfully raised a bit of funding to bring together a team of engineers, researchers and advisors, each offering their specialist skills and knowledge to the project, and I got the chance to get involved.

Continue reading “Update on Lightstage Project”

Bio-Inspired Robotics Conference – SAB2016

Back on 26th August 2016 we drew to a close SAB2016, the 14th international conference of the series on bio-inspiration for robotics and algorithms from more or less any discipline at theory stages, to simulations and empirically applied. For me, nothing can take away my reverberating proud feeling I have for being a part of a diverse, kind and like-minded collection of people with a sheer fascination for nature, paired with the talent and vision to pursue it for the betterment of society and life. I mean, isn’t that the dream!?

Continue reading “Bio-Inspired Robotics Conference – SAB2016”