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What’s a common problem that Philippine organizations have when using location datasets? They’re messy! Misspellings, abbreviations, inconsistent styles and conventions make it tricky for to identify the same place across multiple datasets. And the bigger your datasets get, the more time and work data cleaning takes.
Thinking Machines is developing LinkSight, an open-source web-app that will clean up messy location datasets more quickly. No coding required! Our goal is for LinkSight to do the data cleaning for you so that you can focus on finding insight in your data, making data-driven decisions, and creating impact for your organization.
But to make this happen, we need your help. For the next few months, we will be conducting user testing sessions with our contributors to help us improve the product and get it ready for public release.
Sign up using this form if you want to have early access and participate in alpha testing. You may also read the
for more details.
Got questions? Email us at
Email (Must be Gmail)
We are using Google Sign-in for user access.
Do you have GIS (Geographical Information System) experience?
Do you have experience cleaning up Philippine location names?
In what situations do you need to clean up location datasets? What kind of location datasets do you need to clean up?
Are you willing to do a 30-minute user testing session with us via video conference call?
How did you hear about LinkSight?
Do you want to join the LinkSight contributors Facebook group? If yes, please paste your Facebook profile url here so we can add you or request for access in "LinkSight Contributors" group.
We'll post latest updates there! It's an open space where we can exchange ideas on how to improve LinkSight.
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