EVIDENT H2020– Environmental data for Sweden cities Dataset
Description
EVIDENT H2020- Environmental data for Swedish Cities Dataset
Environmental data from 615 cities in Sweden
Weather, in combination with residential characteristics and electricity consumption, might be useful to consider in association with other datasets. In the instance of EVIDENT, they will be examined in combination with electricity consumption to establish the correlation with weather and to examine if weather conditions contribute and should be considered for policy development.
The data have been collected from 18.10.2021 to 4.05.2023 and refer to 615 Swedish cities. The collection has been carried out with agents created and by calling in API. In the file "Sweden_Cities_Avg_DaySect.xlsx", all cities have averaged from all measures. Also, the day has been divided into 3 sections and the averages apply to each section of the day.
In each city, on average, there are 6 measurements per day. The source dataset is "swedish_cities_environmental.csv.". example
| country | city | temperature | feels_like | temp_min | temp_max | pressure | humidity |
| wind_speed | wind_deg | sunrise | sunset | weather_description |
There are 2 more datasets, "swedish cities environmental_tranformDay.csv" and "swedish cities environmental_week.csv", and refer to transformations made in the original dataset.
The first file is about the day analysis, where the day has been divided into 3 sections and depending on the time of the measurement, a new column has been created in the Day section and can take values 0,1,2. In addition, there is the column day_hours which is the duration of the day in seconds from sunrise to sunset. Finally, there is pressure, humidity and wind speed. In the second file, the column weekday has been added and relates to the day of the week (e.g. Monday), and the daily analysis has been removed.
More information can be found on the public deliverables of the EVIDENT project https://evident-h2020.eu/deliverables/. More specifically, the experiment's theoretical framework and motivation are described in are described in deliverable D1.2 Assessing behavioural biases and financial literacy and deliverable D1.3 Specifications of Big Data Analytics, in section 4 while the final design is reported in D3.2 Implementation of preparatory actions for RCT, surveys and serious game.