Mapbox – Two ways to show a map

For the past few months, I have been charged with visualizing data onto maps using either Google Maps or Mapbox. I chose the latter. After days and nights of struggle, I am pretty close to the finishing line and have gained quite a bit of experience in Mapbox that I want to share. 

Long story short, to map data onto maps, you need to structure data into a specific structure called GEOJSON. It looks like this:

You can put anything in the “properties” key, but the rest essentially have to follow the above format. The coordinates will be used to locate markers or data on the map. 

Let’s say the data that I want to map has 6 different mission areas (see the screenshot above) and my job is to map them onto the map in 6 different colors. 

I have an array that contains 6 different mission areas like this and 6 different colors representing those areas

var missionarea = [area1, area2, area3, area4, area5, area6]
var colorcode = [color1, color2, color3, color4, color5, color6]

One layer approach

#create a map canvas

var map = new mapboxgl.Map({
    container: 'map',
    style: 'mapbox://styles/mapbox/light-v9',
    center: [-96.797885, 39.363438],
    // initial zoom
    zoom: 3.3
});

#load data and create the layer

map.on("load", function() {

    map.addSource('communityData', {   #name the data as communityData
        type: 'geojson',
        data: communityData, #this is the name of your GEOJSON
    });

    map.addLayer({      #add the layer
        "id": "commMap", #your single layer's name
        "type": "circle", #meaning that each item will be a dot
        "source": "communityData", #the name of your data assigned above
        'layout': {},
        'paint': {
            "circle-radius": 8,
            "circle-opacity": 1,
            "circle-color": {
                "property": "Mission Area",
                "type": 'categorical',
                "stops": [ #assign color to respective mission areas
                    [Missionarea[0], colorcode[0]],
                    [Missionarea[1], colorcode[1]],
                    [Missionarea[2], colorcode[2]],
                    [Missionarea[3], colorcode[3]],
                    [Missionarea[4], colorcode[4]],
                    [Missionarea[5], colorcode[5]],
                ]
            }
        }
    });
}

Multiple Layers

In this example, I’ll label each map layer as “show0”, “show1″…”show5”

var showlist = [] //the array of layerIDs, used to categorize layers
var base = "show"
for (var i = 0; i < Missionarea.length; i++) { //populate showlist array
    var text = base + i
    showlist.push(text)
}
var map = new mapboxgl.Map({
    container: 'map',
    style: 'mapbox://styles/mapbox/light-v9',
    center: [-95.957309, 41.276479],
    // initial zoom
    zoom: 6
});

map.on("load", function() {

    map.addSource('communityData', {
        type: 'geojson',
        data: communityData,
    });
    //*********************************** Load partners *****************************************************

    communityData.features.forEach(function(feature) {
        var primary = feature.properties["Mission Area"];
       // iterate through the mission area array
        for (var i = 0; i < missionarea.length; i++) {
            if (primary == missionarea[i]) {
      // assign a name to each layer
                layerID = showlist[i];
                if (!map.getLayer(layerID)) {
                    map.addLayer({
                        "id": layerID, #layer's name
                        "type": "circle",
                        "source": "communityData",
                        "paint": {
                            "circle-radius": 8,
                            "circle-opacity": 1,
                            "circle-color": colorcode[i], #color
                        },
                        "filter": ["all", ["==", "Mission Area", primary]]
                    })
                }
            }
        }
    });

Which approach is better?

If the intention is just to show the dots, there is no difference and it depends on personal preference. However, if your code gets more complicated and as in my case, I had to create at least 6 filters on the map, things will get messy and one approach will no longer allow you to do what you want. Unfortunately, I don’t have that much experience yet to tell you more and I personally believe it’s a case-by-case thing. 

Turn Excel into GEOJSON

My Capstone project requires me to turn Excel in GEOJSON for mapping purposes. Handling and preparing data is 50% of the whole process. I’d like to share what I did step by step, hoping that it will be useful to some who are learning the ropes like I am.

I am using Python as the programming language of choice and Pycharm as IDE. Create a folder on your computer and store the Excel file in question in it. Open the folder in Python and create a new Python file. Here is how it looks on my Pycharm

ExceltoGEOJSON_1

Before we move forward, it’s important to know what a GEOJSON is and how it looks. This website offers a great review on GEOJSON. In terms of structure, a GEOJSON file looks like this

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "Point",
        "coordinates": [0, 0]
      },
      "properties": {
        "name": "null island"
      }
    }
  ]
}

I am pretty sure we can have as many variables under “properties” as we want. The rest should be standard to be followed as possible.

This is how the Excel file looks. Notice that there are coordinates available already. In the future, I’ll work on geocoding an address into coordinates.

ExceltoGEOJSON_2

Let’s start working on the Python code.

import pandas as pd

df = pd.read_excel('CommunityPartner.xls')

Import the “pandas” package. Shorten the package’s name as pd because who would want to repeat a long name many times in the code?

The following line is to read the Excel file into a data frame called df. You can name it however you want. Since the Excel file and the Python code are in the same folder, there is no need to have a directory. Otherwise, it’s necessary to have a full directory.

collection = {'type': 'FeatureCollection', 'features': []}

The next step is to create a shell dictionary. Refer back to the sample structure of a GEOJSON file above to see why I structure the collection variable like that.

df['description'] = df[['Address', 'City', 'State', 'Zip']].apply(lambda x: ' '.join(x.astype(str)), axis=1)

Since we don’t have a full address, the above line is to combine four columns together to form a full address string. The next step is to populate the dictionary

def feature_from_row(CommunityPartner, latitude, longitude, fulladdress, Primary, Website, Phone):
    feature = {'type': 'Feature', 'properties': {'PartnerName': '', 'Address': '', 'marker-color': '',
                                                 'Website': '', 'PrimaryMission': '', 'Phone': ''},
               'geometry': {'type': 'Point', 'coordinates': []}
               }
    feature['geometry']['coordinates'] = [longitude, latitude]
    feature['properties']['PartnerName'] = CommunityPartner
    feature['properties']['Address'] = fulladdress
    feature['properties']['Website'] = Website
    feature['properties']['PrimaryMission'] = Primary
    feature['properties']['Phone'] = Phone
    if Primary == "Economic Sufficiency":
        feature['properties']['marker-color'] = "FF5733"
    elif Primary == "Social Justice":
        feature['properties']['marker-color'] = "FFF033"
    elif Primary == "Health and Wellness":
        feature['properties']['marker-color'] = "74FF33"
    elif Primary == "Environmental Stewardship":
        feature['properties']['marker-color'] = "338DFF"
    elif Primary == "Educational Support":
        feature['properties']['marker-color'] = "CE33FF"
    else:
        feature['properties']['marker-color'] = "FF3374"
    collection['features'].append(feature)
    return feature

Create a function that will undertake the data processing. Between the brackets are the input variables.

feature = {'type': 'Feature', 'properties': {'PartnerName': '', 'Address': '', 'marker-color': '',
                                             'Website': '', 'PrimaryMission': '', 'Phone': ''},
           'geometry': {'type': 'Point', 'coordinates': []}
           }

Create a “feature”variable as above. Try to mirror it in “type” and “geometry” agains the standard GEOJSON (see above) as much as possible. Leave the “coordinate” value as empty to fill in later. Under “properties”, list the keys you want.

feature['geometry']['coordinates'] = [longitude, latitude]
feature['properties']['PartnerName'] = CommunityPartner
feature['properties']['Address'] = fulladdress
feature['properties']['Website'] = Website
feature['properties']['PrimaryMission'] = Primary
feature['properties']['Phone'] = Phone

Time to populate the keys. Remember to key the names of the keys and input variables consistent with what was already posted so far.

You must wonder: what about “marker-color”. You can use the conditional argument to assign values to the variable as follows:

if Primary == "Economic Sufficiency":
    feature['properties']['marker-color'] = "FF5733"
elif Primary == "Social Justice":
    feature['properties']['marker-color'] = "FFF033"
elif Primary == "Health and Wellness":
    feature['properties']['marker-color'] = "74FF33"
elif Primary == "Environmental Stewardship":
    feature['properties']['marker-color'] = "338DFF"
elif Primary == "Educational Support":
    feature['properties']['marker-color'] = "CE33FF"
else:
    feature['properties']['marker-color'] = "FF3374"

If you wonder about the HTML color code, just Google “HTML Color Code” and you’ll see it.

collection['features'].append(feature)
return feature

The first line of the block above dictates that we add every single row of the Excel file to the “features” key of the collection variable. “Return” is a mandatory feature of every function.

geojson_series = df.apply(
    lambda x: feature_from_row(x['CommunityPartner'], x['Lat'], x['Longitude'], x['description'], x['Primary'],
                               x['Website'], x['Phone']),
    axis=1)

jsonstring = pd.io.json.dumps(collection)

The first line is to add every single row of the Excel file to the function so that we can create the string needed for the GEOJSON. The second line is to turn it into json file.

output_filename = 'CommunityPartner.geojson' 
with open(output_filename, 'w') as output_file:
    output_file.write(format(jsonstring))

Name the file however you want and use the second line to write it into GEOJSON. The file product will look like this:

import pandas as pd

df = pd.read_excel('CommunityPartner.xls') #Get the Excel file from static/Excel

collection = {'type': 'FeatureCollection', 'features': []}

df['description'] = df[['Address', 'City', 'State', 'Zip']].apply(lambda x: ' '.join(x.astype(str)), axis=1)


def feature_from_row(CommunityPartner, latitude, longitude, fulladdress, Primary, Website, Phone):
    feature = {'type': 'Feature', 'properties': {'PartnerName': '', 'Address': '', 'marker-color': '',
                                                 'Website': '', 'PrimaryMission': '', 'Phone': ''},
               'geometry': {'type': 'Point', 'coordinates': []}
               }
    feature['geometry']['coordinates'] = [longitude, latitude]
    feature['properties']['PartnerName'] = CommunityPartner
    feature['properties']['Address'] = fulladdress
    feature['properties']['Website'] = Website
    feature['properties']['PrimaryMission'] = Primary
    feature['properties']['Phone'] = Phone
    if Primary == "Economic Sufficiency":
        feature['properties']['marker-color'] = "FF5733"
    elif Primary == "Social Justice":
        feature['properties']['marker-color'] = "FFF033"
    elif Primary == "Health and Wellness":
        feature['properties']['marker-color'] = "74FF33"
    elif Primary == "Environmental Stewardship":
        feature['properties']['marker-color'] = "338DFF"
    elif Primary == "Educational Support":
        feature['properties']['marker-color'] = "CE33FF"
    else:
        feature['properties']['marker-color'] = "FF3374"
    collection['features'].append(feature)
    return feature


geojson_series = df.apply(
    lambda x: feature_from_row(x['CommunityPartner'], x['Lat'], x['Longitude'], x['description'], x['Primary'],
                               x['Website'], x['Phone']),
    axis=1)

jsonstring = pd.io.json.dumps(collection)

output_filename = 'CommunityPartner.geojson' #The file will be saved under static/GEOJSON
with open(output_filename, 'w') as output_file:
    output_file.write(format(jsonstring))

ExceltoGEOJSON_3

This is how the GEOJSON looks:

 
   “type”:“FeatureCollection”,
   “features”: 
       
         “type”:“Feature”,
         “properties”: 
            “PartnerName”:“75 North”,
            “Address”:“4383 Nicholas St Suite 24 Omaha NE 68131.0”,
            “marker-color”:“FF5733”,
            “Website”:null,
            “PrimaryMission”:“Economic Sufficiency”,
            “Phone”:“402-502-2770”
         },
         “geometry”: 
            “type”:“Point”,
            “coordinates”: 
               -95.957309,
               41.276479
            ]
         }
      },
       
         “type”:“Feature”,
         “properties”: 
            “PartnerName”:“A Time to Heal”,
            “Address”:“6001 Dodge St CEC 216 Suite 219C  Omaha NE 68182.0”,
            “marker-color”:“74FF33”,
            “Website”:null,
            “PrimaryMission”:“Health and Wellness”,
            “Phone”:“402-401-6083” …

One important note. If you are a fan of Jupyter Notebook, beware that there may be a problem when it comes to the last step of the process. Here is how the collection variable looks before being dumped into the GEOJSON file.

ExceltoGEOJSON_4

But I ran into errors in the last step. I spent quite some time on fixing it but I couldn’t.

ExceltoGEOJSON_5

Creating the Python code in Pycharm is much easier and produces the same result. It’s even more convenient if you are in the middle of an application development project.

Hope this post helps. Much thanks to appendto and geoffboeing for inspiration.

 

Create a hover effect on Mapbox

I am sharing my experience in trying to create a hover effect on Mapbox. The first thing to do is to read their example and understand what is going on. Let’s unpack a little bit:

<!DOCTYPE html>
<html>
<head>
    <meta charset='utf-8' />
    <title>Create a hover effect</title>
    <meta name='viewport' content='initial-scale=1,maximum-scale=1,user-scalable=no' />
    <script src='https://api.tiles.mapbox.com/mapbox-gl-js/v0.49.0/mapbox-gl.js'></script>
    <link href='https://api.tiles.mapbox.com/mapbox-gl-js/v0.49.0/mapbox-gl.css' rel='stylesheet' />
    <style>
        body { margin:0; padding:0; }
        #map { position:absolute; top:0; bottom:0; width:100%; }
    </style>
</head>

It’s the <head> of the HTML that has scripts from Mapbox. Just follow them and you’ll be fine. Change the text in <title> to have your own page title.

<body>


—- Your real code goes here —–

</body>

Your real work will go between and . The <div> is a container that refers to the map you are working on. The next part is Mapbox token

mapboxgl.accessToken = '<your access token here>';

To get a token, just create a free account on Mapbox. A free account is allowed up to 50,000 requests a month if I am not mistaken. It should be enough for a student or an enthusiast wishing to try it out. Once you have a token, just put it in between ” in the above line.

Let’s have a base map

var map = new mapboxgl.Map({
    container: 'map',
    style: 'mapbox://styles/mapbox/streets-v9',
    center: [-100.486052, 37.830348],
    zoom: 2
});

The “center” feature’s coordinates refer to where you want to focus on. Get your chosen destination’s coordinates and just put them there. Alternate the two figures in coordinates if you don’t get it right in the first try. “Zoom” is how close you look at the chosen destination. The greater the number, the closer the zoom.

var hoveredStateId =  null;

map.on('load', function () {
    map.addSource("states", {
        "type": "geojson",
        "data": "https://www.mapbox.com/mapbox-gl-js/assets/us_states.geojson"
    });

HoveredStateID is a placeholder variable that will be used later for hover effect. The following code block is to load the base map. Just follow the templates. Three things to note here:

  • “state” refers to the object’s name that contains the GEOJSON data. You can name whatever you want
  • “GEOJSON” refers to to the style of the file. Mapping requires GEOJSON files, whether you load it from an external source, like we do in this case, or from a hardcoded file
  • The link that goes with “data” is where the author stores the data.

One note here: if you use Github or any cloud platform to store and source your file, be careful. For instance, let’s look at a file I have on github.

Github_Link

Just copying the usual link when you access your file on Github like that won’t work. To get the link that works, click on “Raw” and here is how it shows on the screen

Github_Content_Link

Copy the link in the browser. It should work.

Back to the HTML. Add the two “map.addLayer” code sections to what you already have. It should look like the below

map.on('load', function () {
    map.addSource("states", {
        "type": "geojson",
        "data": "https://www.mapbox.com/mapbox-gl-js/assets/us_states.geojson"
    });

    map.addLayer({
        "id": "state-fills",
        "type": "fill",
        "source": "states",
        "layout": {},
        "paint": {
            "fill-color": "#627BC1",
            "fill-opacity": ["case",
                ["boolean", ["feature-state", "hover"], false],
                1,
                0.5
            ]
        }
    });

    map.addLayer({
        "id": "state-borders",
        "type": "line",
        "source": "states",
        "layout": {},
        "paint": {
            "line-color": "#627BC1",
            "line-width": 2
        }
    });

The first addLayer is for the polygon itself while the second one is for the lines between the states. “id” refers to the name of the layer for future reference. Remember to tie the “source” value back to the name of map.addSource. In this case, it’s “states”. The rest is a Mapbox standard template for hover effect. You can change the color whenever you feel like.

The next step is to work on “hover effect”. Add the following code to the end of the previous block

    map.on("mousemove", "state-fills", function(e) {
        if (e.features.length > 0) {
            if (hoveredStateId) {
                map.setFeatureState({source: 'states', id: hoveredStateId}, { hover: false});
            }
            hoveredStateId = e.features[0].id;
            map.setFeatureState({source: 'states', id: hoveredStateId}, { hover: true});
        }
    });

    // When the mouse leaves the state-fill layer, update the feature state of the
    // previously hovered feature.
    map.on("mouseleave", "state-fills", function() {
        if (hoveredStateId) {
            map.setFeatureState({source: 'states', id: hoveredStateId}, { hover: false});
        }
        hoveredStateId =  null;
    });

The first thing to notice is here:  map.on(“mousemove”, “state-fills”, function(e) {

“State-fills” is the “id” of the polygon layer mentioned previously. So whatever name is chosen for that addLayer, it should be used here.

source: ‘states’

In this case, ‘states’ refers to the source of the data in the map.addSource section above. Remember to use the same reference name for the source. The rest is just a standard template. If you have time, feel free to explore. I am under pressure to deliver features for my Capstone, so I just prefer not touching or changing any of it.

Here is an important note. If you don’t follow, the hover effect won’t work. I use the same code as Mapbox’s example, just changing the GEOJSON source. The hover effect doesn’t work as you can see below:

The key is the data source. Let’s look at the data that Mapbox uses. Here is the tree view of the first item in the polygon array, just to show its structure

GEOJSON_2

Here is the structure of the data I used that led to the unsuccessful “hover effect”

GEOJSON_3

Notice the difference? As far as I am concerned, the hover template in question needs the data to have a certain structure. Otherwise, the code won’t work. Now, there should be other ways to go around this, but if you don’t have time, I’d suggest modifying the data to mirror Mapbox’s example. Here is the structure of my modified data

GEOJSON_4

Does the code work? You bet!

GEOJSON_5

Hopefully this post will be useful to starters like I am.