I would like to take raster (say a JPG) cut it in subregions and associate each reagion to a row in a pandas dataframe, so that the image can be visualized according to some filters on the data. I see this is pretty standard using bokeh in python, but I do not find tutorial on how to prepare the raster for this. All explanation somehow use premade data such as the counties of texas example in bokeh ...
So, what should I do to my custom image to transform it into something can be used with bokeh to make an interactive map?
Related
Recently I have been reading a paper called Modeling Taxi Drivers’ Behaviour for the Next Destination Prediction. There is a figure(Fig.1) that I wonder how to draw. Based on what I know, it may be drawn by Python. Then what library of Python should I use to draw such a heatmap?
Thanks a lot in advance for your time and your expertise.
Best Regards
I have built something very similar for myself in the past. However, as you are just wondering how this would be drawn and not exactly asking how to do it with Python, I will share how I did it.
1. Building Grid:
The grids on the map are squares of X-size latitude and longitude. In short, those are latitude and longitude grids. I used an interactive map library named leaflet.js to build the world map with an overlay of latitude and longitude grids. This is the tutorial I followed from the leaflet: https://leafletjs.com/examples/choropleth/
Remember, you have to and can build your version of the grid to overlay on a world map using GeoJSON as discussed in the tutorial. At least, when I was building, there wasn't a publicly available version of the lat/long square grid.
2. Showing Colors (Heatmap):
Once you build the grid with GeoJSON, the leaflet can take the whole GeoJSON as it is and overlay it on any map of your choice. That means you can put the numbers(aka data) for each grid in the GeoJSON. This part is also shown in the same tutorial.
For my project, I used to create a complete GeoJSON formatted file with normalized data in Python and then visualize it in leaflet.js. Below is an example of what I have built using these tools.
I've been doing quite a bit of google searches and I cannot seem to get what I am trying to achieve. I have two datasets, one with polygon coordinates and shape IDs and another dataset with shape IDs and categorical data. I really would like to create a custom map type of visualization with data that has nothing to do with geography. I can't seem to find a python library that will help me achieve this, does anyone have any ideas?
Ideally the viz is interactive where the user can filter through shapes and hover over shapes and obtain other info from the categorical data set. Thank you!
See example I made using JMP
What is the best way to combine an image and some associated metadata to be displayed in tabular form below the image as given in this example? I need to use Python but not sure if Matplotlib will be needed.
I'm fairly new to folium so this might be a bit noobish but I'm currently trying to plot several heatmaps of different data-points and add the ability to switch between the heatmaps all on the same plot. So for example I have such:
# The base map
hmap = folium.Map(location=[38.908111, -77.008871], tiles="Stamen Terrain", zoom_start=12)
# And each layer
# Homicide
HeatMap(list(zip(crime_homicide.LATITUDE.values, crime_homicide.LONGITUDE.values))).add_to(folium.FeatureGroup(name='Homicides').add_to(hmap))
# Robbery
HeatMap(list(zip(crime_robbery.LATITUDE.values, crime_robbery.LONGITUDE.values))).add_to(folium.FeatureGroup(name='Robbery').add_to(hmap))
# Assault
HeatMap(list(zip(crime_assault.LATITUDE.values, crime_assault.LONGITUDE.values))).add_to(folium.FeatureGroup(name='Assault').add_to(hmap))
folium.LayerControl(collapsed=False).add_to(hmap)
folium.GeoJson(dc_shape).add_to(hmap)
I tried using folium's FeatureGroup functionality but it looks like thats only specific markers as opposed to whole maps. Is there a way to switch between different maps if they're all heatmaps?
Your code seems fine.
Try this -
hmap.add_child
Or you can try heatmapwithtime as well, specifying different metrics which you can adjust in realtime to see different heatmaps.
But,FeatureGroup() will not seem to work with HeatMapWithTime and adding layers directly to the heatmap results in multiple time sliders on the side when there should be only one (common) time slider for all added layers.
So if you want to have a single control you'll have to put all your data in a single geojson and use that.
Why do you add a feature group? If you want to be able to select which instance of HeatMapWithTime you want to display, you can add both add them to the map, and they should both turn up in layer control.
m = Map()
HeatMapWithTime(data1).add_to(m)
HeatMapWithTime(data2).add_to(m)
FYI, a feature group is meant to group items and display them together. The items themselves don't get added to the map directly. For example:
fg = FeatureGroup().add_to(m)
fg.add_child(Item1)
fg.add_child(Item2)
Also this is the link, might help you :)
https://python-visualization.github.io/folium/plugins.html
I have data in the format (latitude, longitude, value). I want to plot (lat, long) -> value on a map of the city. Something like the following images:
I've already tried the following:
Python's Matplotlib: Unable to find required functions
Plotly
r-barplots on map, RG-histogram-bar-chart-over-map.
plot-3d-bars-on-a-map-in-matlab: This will do, but I'm trying to find a similar thing in python
D3 map histogram: This allows me to plot
city-wise, but not within a city.
I posted above question and then, found interesting plotting libraries.
Cesium : An open-source JavaScript library for 3D globes and maps.
ArcGIS: This one is paid (60 days free trial is available), but provide a wide variety of beautiful visualizations on 2D maps and 3D globes
How about Basemap?
It is a matplotlib extension, so it has got all its features to create data visualizations and adds the geographical projections and some datasets to be able to plot coastlines, countries directly from the library.