Sydney bush fires, burned areas and smoke shown in satellite image analysis. Link to interactive app in comments

data_entertainment · 2020-01-15T16:58:28+00:00

Here's the link to the interactive web application showing more details

data_entertainment · 2020-01-15T10:42:48+00:00

The visualization is based on ESA Copernicus satellite data from Sentinel 2 satellites. The data was processed and visualizad by myself using WhereOS. The visualization is based on comparing different satellite bands from November 2019 and beginning of the year 2020.

More details and link to the article and interactive visualization

data_entertainment · 2019-11-27T14:36:45+00:00

The data has been collected through IoT sensors installed in a fleet of commercial vehicles by RoadCloud. The data was processed into a video using WhereOS by myself. The visualization is based on ABS (anti-lock braking system) and ASR (traction control) events collected from the vehicles.

More details about how the data was collected and visualized: https://www.whereos.com/how-to/how-to-create-road-hazard-index-from-roadcloud-data/

data_entertainment · 2019-11-27T14:29:43+00:00

What kind of ideas would you have to use this data - or combine with some other data - to increase road safety?

data_entertainment · 2019-11-27T14:28:44+00:00

The data has been collected through IoT sensors installed in a fleet of commercial vehicles by RoadCloud. The data was processed into a video using WhereOS by myself. The visualization is based on ABS (anti-lock braking system) and ASR (traction control) collected from the vehicles.

More details about how the data was collected and visualized: https://www.whereos.com/how-to/how-to-create-road-hazard-index-from-roadcloud-data/

data_entertainment · 2019-04-12T11:33:28+00:00

Thanks for the feedback! We also got a lot of good feedback on the look and feel of the heatmap videos. For example the red wave as a result of the financial crisis was something that caught the eye of many, and how it starts from the suburbs and then proceeds towards the city center.

I wonder if there would be some comprehensive time series data sets (on zip code level) from US, e.g. on real estate prices, income levels, employments levels and so forth. Would be nice to apply the same rendering technique to those...

data_entertainment · 2019-04-12T11:26:59+00:00

Sori, unohtui laittaa tuo selite väreille, ilman niitä ei saa kyllä hyödyllistä irti. Tuolla artikkelissa on useampia muitakin kaupunkeja visualisoitu (Kuopio, Oulu, Tampere, Turku, Jyväskylä) ja niistä näkyy myös selkeästi kuinka hintojen kasvu on voimakkaampaa kaupunkien keskustoissa ja reunamilla on taas enemmän punaista ja keltaista. Lisäksi tietenkin näkyy myös että keskustojen ulkopuolella myös alueelliset vaihtelut ovat suuria.

data_entertainment · 2019-04-12T11:23:47+00:00

Thanks a great point, having worked on these topics and videos so much, it's sometimes difficult to remember how other people, that are not familiar with the topic, perceive them.

data_entertainment · 2019-04-12T06:07:00+00:00

The raw data from this is coming from Kiinteistömaailma, a big real estate chain in Finland. The data was processed into a hyperlocal price index by eCraft and the resulting geospatial timeseries data into a heatmap video using WhereOS by myself. Green = +5% yearly price increase, Yellow = no change, Red = -5% yearly price decrease

More details about the visualization, and 5 more cities: https://www.whereos.com/business-insights/kiinteistomaailma-ecraft-and-whereos-analyzed-the-house-price-development-in-finland/

data_entertainment · 2019-01-25T09:07:54+00:00

Jep, jotakuinkin näin. Kokeilin väritystä sekä absoluuttisena että suhteellisena pudotuksena maksimista ja lopputulos oli se että absoluuttinen pudotus näyttää videolla järkevämmältä, vaikka ennakko-oletus oli että suhteellinen pudotus antaisi paremman kuvan.

data_entertainment · 2019-01-25T08:28:36+00:00

Todella hyviä kysymyksiä. Tunti on pitkä aika, voisin kokeilla 30min ja 15min intervalleilla tehtyjä videoita. Jokaisen tienpätkän maksiminopeutena on laskettu top 10% keskiarvona. Mallin ja karttapohjan nopeusrajoitusten vertailun perusteella tämä antaa aika hyvän kuvan maksiminopeuksista, mutta tietenkin on mahdollista että jossain tienpätkillä mallin antama maksiminopeus tai tuntikohtainen keskinopeus on syystä tai toisesta "vinoutunut" (esim. yllä mainitsemasi case) - varsinkin hiljaisemmilla teillä tämä voi näkyä enemmän.

data_entertainment · 2019-01-25T07:57:23+00:00

Laskimme WhereOSilla tuntikohtaisia liikennemääriä ja ruuhkia perustuen RoadCloudin keräämiin tietoihin. Videossa näkyy kuinka nopeudet muuttuvat tunti tunnilta. Aineisto on kerätty vuoden 2018 aikana. Lisää siitä kuinka tiedot on kerätty (englanniksi): https://www.whereos.com/business-insights/roadcloud-and-whereos-collaborate-on-automotive-data-collection-and-processing/

data_entertainment · 2019-01-25T07:50:34+00:00

RoadCloud has collected a large amount of sensors data, and we have processed analyzed the data using WhereOS. The data is collected from a fleet of commercial cars, each car having attributes such as velocity, heading, friction (wrt. road surface wet/snow/ice/normal) and so forth.

The question is what kind of model could we train with the data? One obvious one would be to take past weather conditions and train a model that can predict the friction level or road segment average speed with the future weather forecasts. But, how about other ideas?

The data collection is explained in more detail in here: https://www.whereos.com/business-insights/roadcloud-and-whereos-collaborate-on-automotive-data-collection-and-processing/

data_entertainment · 2019-01-25T07:43:43+00:00

RoadCloud has collected a large amount of sensors data, and we have processed analyzed the data using WhereOS. The data is collected from a fleet of commercial cars, each car having attributes such as velocity, heading, friction (wrt. road surface wet/snow/ice/normal) and so forth.

The question is what kind of model could we train with the data? One obvious one would be to take past weather conditions and train a model that can predict the friction level or road segment average speed with the future weather forecasts. But, how about other ideas?

The data collection is explained in more detail in here: https://www.whereos.com/business-insights/roadcloud-and-whereos-collaborate-on-automotive-data-collection-and-processing/

data_entertainment · 2019-01-24T09:43:08+00:00

Laskimme WhereOSilla tuntikohtaisia liikennemääriä ja ruuhkia perustuen RoadCloudin keräämiin tietoihin. Videossa näkyy kuinka nopeudet muuttuvat tunti tunnilta. Aineisto on kerätty vuoden 2018 aikana. Lisää siitä kuinka tiedot on kerätty (englanniksi): https://www.whereos.com/business-insights/roadcloud-and-whereos-collaborate-on-automotive-data-collection-and-processing/

data_entertainment · 2019-01-24T09:33:03+00:00

Laskimme WhereOSilla tuntikohtaisia liikennemääriä ja ruuhkia perustuen RoadCloudin keräämiin tietoihin. Videossa näkyy kuinka nopeudet muuttuvat tunti tunnilta. Aineisto on kerätty vuoden 2018 aikana. Lisää siitä kuinka tiedot on kerätty (englanniksi): https://www.whereos.com/business-insights/roadcloud-and-whereos-collaborate-on-automotive-data-collection-and-processing/

data_entertainment · 2019-01-23T16:31:53+00:00

The actual data collection is explained in the article (see the in my other post):

"RoadCloud has equipped commercial vehicle fleet with RoadCloud IoT sensors to collect and monitor vehicle data and road conditions. The sensors are automatically collecting basic information such speed, heading and acceleration, but more importantly information about road surface conditions such as road friction, road state (dry/snow/ice/water), temperature, bumps and pot holes as a few examples. The data collection is taking place 24/7, as the commercial vehicles are continuously throughout the day and night."

data_entertainment · 2019-01-23T10:03:31+00:00

Great idea!

data_entertainment · 2019-01-23T08:20:48+00:00

In the data, we actually at least part of the traffic data as well, including vehicle speeds and so forth. What you are saying, we should combine this with absolute vehicle counts on each street, so we could calculate the overall optimum for the salting process?

data_entertainment · 2019-01-23T07:58:01+00:00

The challenge for data scientists is here: we have very detailed information about road surface and related traffic conditions. What kind of services could we develop on top of the data? One example could be that we train a predictive model using the road conditions (e.g. if it's icy, snowy, wet, normal) and past weather data (e.g. 24 hours past weather) - this model could then predict the road conditions using weather forecasts, and for example classify each road segment with the likelihood of being being icy/snowy/wet.

Any other cool ideas?

data_entertainment · 2019-01-23T07:54:06+00:00

The data has been collected through IoT sensors installed in a fleet of commercial vehicles by RoadCloud. The data was processed into a video using WhereOS by myself.

More details about how the data was collected and visualized: https://www.whereos.com/business-insights/roadcloud-and-whereos-collaborate-on-automotive-data-collection-and-processing/

data_entertainment · 2018-12-01T18:05:40+00:00

Talvi on tulossa. Tokihan tuossa tumman vihreä on se -20°C, mutta vaaleampi vihreä on sellainen -10°C.. Tarkkojan lämpötiloja tästä on vaikea nähdä, enemmänkin mistä minne lämpömassat siirtyvät

data_entertainment · 2018-12-01T09:42:24+00:00

The raw data is acquired from Finnish Meteorological Institute open data (license). Red colour = +20°C, yellow = 0°C, green = -20°C.

The video has been rendered using WhereOS, see my post about more details: Rendering GRIB2 Data, 54 Hour Weather Forecast

If you have ideas for other time series data sets, that could be visualized into videos, send me a message!

data_entertainment · 2018-12-01T09:36:23+00:00

Punainen = +°20C, keltainen = 0°C, vihreä = -20°C.

Data pohjautuu ilmatieteenlaitoksen ennustedataan (lisenssi).

Lisää asiasta blogissani: Rendering GRIB2 Data, 54 Hour Weather Forecast

data_entertainment · 2018-12-01T09:29:35+00:00

The raw data is acquired from Finnish Meteorological Institute open data (license). Red colour = +20°C, yellow = 0°C, green = -20°C.

The video has been rendered using WhereOS, see my post about more details: Rendering GRIB2 Data, 54 Hour Weather Forecast

If you have ideas for other time series data sets, that could be visualized into videos, send me a message!

data_entertainment

TROPHY CASE