By: Zahra H. Mohamed for SA8905 @RyersonGeo
You can download the code here!
Introduction
Natural disasters are major events that result from natural processes of the planet. With global warming and the changing of our climate, it’s rare to go through a week without mention of a flood, earthquake, or a bad storm happening somewhere in the world. I chose to make my web map on natural disasters, because it is at the front of lot of people’s minds lately, as well as there is reliable and historical public data available on disasters around the world. My main goal is to make an informational and easy to use web page, that is accessible to anyone from any educational level or background. The web page will display all of the recorded natural disasters around the world over the past 68 years, and will allow you to see what parts of the world are more prone to certain types of disasters in a clear and understandable format.
In order to make my web map I used:
- Javascript – programming language
- HTML/CSS – front-end programming language and stylesheets
- Leaflet – a javascript library or interactive maps
- JQuery – a javascript framework
- JSCharting – a javascript charting library that creates charts using SVG (Scalable Vector Graphics)
Data & Map Creation
The data for this web map was taken from: Geocoded Disasters (GDIS) Dataset, v1 (1960-2018) from NASA’s Socioeconomic Data and Applications Centre (SEDAC). The data was originally downloaded as a Comma-separated values (CSV) file. CSV files are simple text files that allow for you to easily share data, and generally take up less space.
A major hurdle in preparing this map was adding the data file onto the map. Because the CSV file was so large (30, 000+). I originally added the csv file onto mapbox studio as a dataset, and then as tiles, but I ended up switching to Leaflet, and locally accessing the csv file instead. Because the file was so large, I decided to use QGIS to sort the data by disaster type, and then uploaded them in my javascript file, using JQuery.
Data can come in different data types and formats, so it is important to convert data into format that is useful for whatever it is you hope to extract or use it for. In order to display this data, first the markers data is read from the csv file, and then I used Papa Parse to convert the string file, to an array of objects. Papa Parse is a csv library for javascript, that allows you to parse through large files on the local system or download them from the internet. Data in an array and/or object, allows you to loop through the data, making it easier to access particular information. For example, when including text in the popup for the markers (Figure 2), I had to access to particular information from the disaster data, which was very easy to do as it was an object.
Code snippet for extracting csv and creating marker and popup (I bolded the comments. Comments are just notes, they are not actually part of the code):
// Read markers data from extreme_temp.csv
$.get('./extreme_temp.csv', function (csvString) {
// Use PapaParse to convert string to array of objects
var data = Papa.parse(csvString, { header: true, dynamicTyping: true }).data;
// For each row in data, create a marker and add it to the map
for (var i in data) {
var row = data[i];
// create popup contents
var customPopup = "<h1>" + row.year + " " + row.location + "<b> Extreme Temperature Event<b></h1><h2><br>Disaster Level: " + row.level + "<br>Country: " + row.country + ".</h2>"
// specify popup options
var customOptions =
{
'maxWidth': '500',
'className': 'custom'
}
var marker = L.circleMarker([row.latitude, row.longitude], {
opacity: 50
}).bindPopup(customPopup, customOptions);
// show popup on hover
marker.on('mouseover', function (e) {
this.openPopup();
});
marker.on('mouseout', function (e) {
this.closePopup();
});
// style marker and add to map
marker.setStyle({ fillColor: 'transparent', color: 'red' }).addTo(map);
}
});
I used L.Circlemarker ( a leaflet vector layer) to assign a standard circular marker to each point. As you can see in Figure 1 and 3, the markers appear all over the map, and are very clustered in certain areas. However, when you zoom in as seen in Figure 3, the size of the markers adjusts, and they become easier to see, as you zoom into the more clustered areas. The top left corner of the map contains a zoom component, as well these 4 empty square buttons vertically aligned, which are each assigned a continent (just 4 continents for now), and will navigate over to that continent when clicked.
The bottom left corner of the map contains the legend and toggle buttons to change between the theme of the map, from light to dark. Changing the theme of the map doesn’t alter any of the data on the map, it just changes the style of the basemap. Nowadays almost every browser and web page seems to have a dark mode option, so I thought it would be neat include. The title, legend and the theme toggles, are all static and their positions on the web page remain the same.
Another component on the web page is the ‘Disaster Fact’ box on the bottom right corner of the page. This textbook is meant display random facts about natural disaster over a specified time interval. Ideally, i have variable that contains an array of facts in a list, in string form. Then use the setInterval(); function, and a function that generates a random number, that is the length of the array – 1, and use that as an index to select one of the list items from the array. However, for the moment the map will display the first fact after the specific time interval, when the page loads, but then it remains on the page. But refreshing the page, will cause for the function to generate another random fact.
One of the component of my web map page, that I will expand on, is the chart. For now I added a simple pie chart using JSCharts to display the total number of disasters per disaster type, for the last 68 years. Using JSCharts as fairly simple, as you can see if you take a look at the code for it in my GitHub. I calculated the total number of disasters for each disaster type by looking at the number of lines in each of my already divided csv files, and manually entered them as the y values. However, normally in order to calculate this data, especially if it was in one large csv file, I would use RStudio.
Something to keep in mind:
People view websites on different platform nowadays, from laptops, to tables and iPhones. A problem with creating web pages is to keep in mind that different platform for viewing web pages, have different screen sizes. So webpages need to be optimized to look good in differ screen sizes, and this is largely done using CSS.
Looking Ahead
Overall my web map is still in progress, and there are many components I need to improve upon, and would like to add to. I would also like to add a bar chart that shows the total number of disasters for each year, for each disaster type , along the bottom of the map, with options to toggle between each disaster type. Also I would like to add a swipe bar that allows you to see the markers on the map based on the year. A component of the map I had trouble adding was an option to hide/view marker layers on the map. I was able to get it to work for just one marker for each disaster type, but it wouldn’t work for the entire layer, so looking ahead I will figure out how to fix that as well.
There was no major research question in making this web page, my goal was to simply make a web map that was appealing, interesting, and easy to use. I hope to expand on this map and add the components that I’ve mentioned, and fix the issues I wasn’t able to figure out. Overall, making a web page can be frustrating, and there is a lot of googling and watching youtube videos involved, but making a dynamic web app is a useful skill to learn as it can allow you to convey information as specifically and creatively as you want.
