Gala Camacho
Head of Analytics
Neighbourlytics
location_on Australia
Member since 1 year
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Gala Camacho
Specialises In (based on submitted proposals)
Gala is a mathematician and programmer using data analytics to improve the way we understand cities. With a diverse background in secondary education, volunteering, optimisation and all-things data, Gala is enthusiastic to find solutions to interesting problems and innovative ways to visualise data.
At Neighbourlytics, Gala investigates what information can be extracted from urban data sources to define and visualise the unique local identity of a neighbourhood.
Gala is driven to positively impact her community through her skills and her voice. As such, she is involved with multiple organisations aimed at making the world (but particularly, the tech-world) a better place for women.
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Using Social Media Data to Explore Place Activity During the 2018 Gold Coast Commonwealth Games
30 Mins
Case Study
Intermediate
Why do people choose to live in one neighbourhood over another? Every day government makes decisions that can change the way a neighbourhood operates and feels. Understanding the impact that these decisions have is convoluted and hard to measure.
In April, the Gold Coast held the 2018 Commonwealth Games. These events, usually advertised as urban renewal or regeneration projects, have a lasting impact on the neighbourhoods where they take place. Usually, there is a strong push to predic the impact of the games through economic assessments and surveys, however, once the games are happening, is that impact tracked? Sometimes, further economic assessments are produced years after the event which evaluate whether the impact was as expected. Can we do better?
Social data can provide us with more timely evidence of whethere the event is activating the economy as expected, or maybe highlight issues that can be resolved.
Using social media data we will explore Facebook places data during and after the games, ultimately generating a dashboard to help us visualise change by giving us the ability to filter and aggregate the data. We will manage the data using Python’s Pandas, generate quick visualisations using Plotly, and finish off by spinning up a dashboard using Plotly’s Dash.
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