Blyncsy Pulse - Weather UI

Project Role:

Product Manager & UX Designer

Team Composition:

Leadership: VP of Technology, VP of Product 
Design: 1 UX designer (me)
Eng: 1 software engineer

01 Introduction

Blyncsy is the leader in movement data intelligence. Blyncsy collects, aggregates and analyzes data on traffic, roads, weather and other datasets, which leads to better, safer, more efficient transportation decisions.

02 Business Objective

The weather component of the Overview page demos well in a sales setting. We want to continue providing value to the sales demo process. With that in mind, we coupled weather analysis with Route Analyzer. Route Analyzer is a tool where an engineer can select specific sensors (which create routes) from their project and monitor its historical data. Route Analyzer shows speed, travel time, delay, and matches of mobility data. This data gives engineers an idea of how their projects are performing at specific times of the day. Think of it like Strava where one can select a specific trail segment to compare their past results and even compare your PR’s with your buddies. Except, Route Analyzer doesn’t encourage you to speed to past your friends.

Implementing this components of analysis will provide engineers a holistic view of route behavior compared to weather. Our aim was to keep engineers engaged with the Blyncsy Pulse data set.

Screenshot of empty state Route Analyzer

03 User Objective

As a user Eric the engineer wants to see how weather may have impacted a change to travel time or delay on a route. Remember, Eric’s job is to improve safety or efficiency. Weather as we knew in Utah changes very quickly. At times unpredictable weather creates absolute chaos on major arteriels for commuting. So, its beneficial that Eric sees the impact of inclement on his roads.

  • Use case 1 — Eric needs to view route data with weather analysis in one experience. Eric will be able to view route data along with weather analysis variables including temperature, precipitation, and wind.
  • Use case 2— Eric sees a significant change in route behavior. Eric needs additional analysis parameters to investigate the change. Eric digs deeper into the data with weather analysis and determines the problem was an increase in precipitation which affected the regular flow of traffic.

04 Key decisions made

  1. Weather and Route Analyzer data are viewable in single chart. (E.g., travel time, precipitation, wind speed)
  2. Hover on either chart is connected
  3. Weather configuration lives in Data Analysis in Route Analyzer
  4. Weather variables must show: (Temperature: avg. temp, high temp, low temp — Precipitation: total precip — Wind: avg. wind mph, high wind mph)
Weather UI

Additional key decisions were choosing a weather API to pull from. We soon learned that the weather API we initially had expired and we couldn’t connect them to renew. The Weather Channel acquired WU and seemed to have shut down their support for WU. We were in a frenzy to choose an API that fulfilled each of our data requirements. We built the UI to be able to pull data from small to large datasets. The weather must be displayed in 15 minute, Hour, Day, Week, Month, and Year intervals. All API’s don’t work the same. So, we researched a few and narrowed down the one we finally selected. It was one wrench of many and seem to enter into the picture during the last week of a sprint.

05 Celebrations & Conclusions

For me, it’s important to note that this was the first product I worked on where I had led a team to launch day. I truly didn’t know what to expect. It was this experienced where I realized that product development is a messy complex process. There were several variables to turn this into a success. Some variables are people. I learned that the human component of product development needs to be taken seriously and with care. Thankfully I had a supportive team where each individual had the same goal to build an impactful product. We learned a lot along the way and made an effort to improve our processes moving forward.

06 Quality Assurance Testing

This part is a little monotonous, but its more for me to document how the Weather UI was tested to go out into the real world.

Staging Scenario testing

Scenario 1 Long range test: Auto Time Series

Date Range: 4/1/18–4/31/18
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Weather data populated successfully

Scenario 2 Long range test: Day Time Series

Date Range: 4/1/18–4/31/18
Excluded days: (Tuesdays & Thursdays)
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Excluded days showed no data with gray bars displayed successfully
Weather data populated successfully

Scenario 3 Mid range test: Auto Time Series

Date Range: 4/15/18–4/28/18
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Weather data populated successfully

Scenario 4 Mid range test: Day Time Series

Date Range: 4/15/18–4/28/18
Excluded days: (Tuesdays & Thursdays)
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Excluded days showed no data with gray bars displayed successfully
Weather data populated successfully

Production Live Scenario testing

Scenario 1 Long range test: Auto Time Series

Date Range: 5/1/18–5/31/18
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Weather data populated successfully

Scenario 2 Long range test: Day Time Series

Date Range: 5/1/18–5/31/18
Excluded days: (Tuesdays & Thursdays)
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Excluded days showed no data with gray bars displayed successfully
Weather data populated successfully

Scenario 3 Mid range test: Auto Time Series

Date Range: 5/15/18–5/28/18
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Weather data populated successfully

Scenario 4 Mid range test: Day Time Series

Date Range: 5/15/18–5/28/18
Excluded days: (Tuesdays & Thursdays)
Route1: 4 Sensors
Route2: 3 Sensors
Results:
Data in chart populated successfully
Data in table populated successfully
Excluded days showed no data with gray bars displayed successfully
Weather data populated successfully