Skip to content

Introduction

I feel okay in 10-30 ºC, optimum 22ºC, 12-36ºC with fast wind. However let a bit colder to account for winter nights.

Use some "equivalent temp", taking humidity, wind, and sunlight into account. (Humidity exaggerates the temperature, a little wind goes a long way, sunlight can fry you)

Country list

  • Switzerland
  • Texas
  • Andorra
  • Holand
  • Lisbon, Porto
  • Georgia (no US)
  • Florida
  • Australia
  • Puerto Rico - USA without USA taxes
  • Estonia
  • Nevada
  • Panama
  • Ireland
  • Isla Margarita
  • Tennessee
  • New Hampshir
  • Singapore (Cool but $), Malaysia?

Requirements - Filters

This section outlines the key criteria used to filter potential locations, categorized by climate, geographical, and political/economic factors.

Climate Filters

These filters focus on environmental conditions to ensure a comfortable and sustainable living environment.

  • Temperature

    • t_max = average of all max temps for each day within the warmest month. Must be < 30 celsius
    • t_min = average of all min temps for each day within the coldest month. Must be > 3 celsius. Definitely above 0.
    • ```python def points_for_temp(temp: float, t_min: float = 3, t_max: float = 30) -> float: # The further you get from the extremes, the more points you'll lose. The optimum temp is the average of t_max and t_min return - (t_min-temp)2 - (t_max-temp)2

    place.points += points_for_temp(place.t_max) place.points += points_for_temp(place.t_min) `` - Rainfall -rain_speed_max =90th percentileq(0.9)of max rain_speed (This filters crazy not normal storms, but cares about the maximum) between 6:00 and 21:00 - Filter out the places with top half rain_speed_max - (Plot this in map and histogram may be interesting) - Humidity -humidity_max =average humidity of the most humid month. - Filter out the top half - Cloud cover - Health and psychology reasons - Average .68, Madrid has 0.4, Los Angeles 0.32, Lisbon 0.48) -cloud_cover_max =maximum value of 60-day moving average of cloud cover, over a year -cloud_cover_max < 0.6- Check what kind of clouds are being checked etc. How do translucid high ones affect the value. - Filter entire climates according to [Köppen–Geiger climate classification system](https://en.wikipedia.org/wiki/K%C3%B6ppen_climate_classification) - Only take these ones:Cfswbc: Cfb Csb Csc Cwb Cwc` - C: Warm temperate - fsw: Fully humid, summer dry, winter dry. - abc: a: hot, b: warm, c:cold summer. - Cfa: Warm Oceanic (Humid Subtropical) - Cwb: Tropical Highland, Csb: Warm Mediterranean Climate - Csa: Hot Mediterranean Climate.

Geographical Filters

These filters consider the physical characteristics of the land and its surroundings.

  • Visibility, many hours with sunset/sunrise light
    • python def horizon_angles(coordinates: tuple[float, float]) -> List[float]: ''' Takes a position and returns an array with the angles of the horizon at your location, for every orientation ''' def horizon_angle_sun_average(coordinates: tuple[float, float]) -> float: ''' Return the average horizon angle between the sunrise and sunset sun orientation at winter, which are the angles we care about. '''
    • Highest elevation in the SouthEast-SouthWest directions (from winter sunrise sun orientation to sunset)
      • You can have obstacles like a mountain at east, but leave it free at south-east to have sunlight at winter
      • horizon_angle_sun_average(...) < 10 deg? or just filter out top 25%
  • Filter places that are southwards of the top of the hill for colder climates, or northwards for warmer climates. The slope break (change of slope) is a good place.

Political & Economic Filters

These filters address the governmental and financial aspects of a location.

  • Filter government permit to build an independent home with land around it.
  • Politics and Economics
    • Free country with powerful economy.

Expected Results

  • best temp: latitudes within the Ferrel Mid-latitude cell, 30 to 60 deg.
    • Close to 30 very dry, close to 60 very humid.
    • Wind generally from west to east
  • Big mass of water at the west to stabilize air temperature, and lower the horizon

Visualizations

Global air circulation - Ferrel Mid-latitude cell

Earth Global Circulation - en.svg Untitled.png

Max temperature of warmest month

20230902161523.png

Average temperature

Annual Average Temperature Map.png

Cloud Cover

Cloud cover

Diurnal air temperature variation

Diurnal air temperature variation

Köppen–Geiger climate classification system

Köppen–Geiger climate classification system

Nomad index - Best cities for digital nomads

cities.jpg

References

Datasets

  • https://www.numbeo.com/cost-of-living/
  • https://docs.kinetica.com/7.1/guides/quickstart-guide/
  • https://datasetsearch.research.google.com/
  • https://ourworldindata.org/
  • https://data.world/datasets/geography
  • https://freegisdata.rtwilson.com/
  • https://power.larc.nasa.gov/
  • https://power.larc.nasa.gov/api/pages/?urls.primaryName=Hourly
  • https://archive.ics.uci.edu/ml/index.php
  • https://data.worldbank.org4
  • https://www.kaggle.com/datasets

the CHELSA climate dataset (Karger et al., 2018; Data from: Climatologies at high resolution for the earth’s land surface areas, Dryad, Dataset.) which is part of the PaleoClim (1979–2013) database (Brown et al. 2018; SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic, and species distribution model analyses. Meth. Ecol. Evol. 5:694–700.). Additionally, we used data from land cover from the European Space Agency GlobCover Project, which is a helpful addition for landscape ecology studies in particular.