A response is not a static thing and there are constant changes in the information being received. Maps show the spatial dimension (the where) of a response but they, as well as other visuals, can also be used to show change over time or the temporal dimension. Potentially any data that has a date or timestamp can be used.
Below is are some approaches on how to use and visualise temporal data.
Cyclones and storms - a cyclone will move along a particular path (track) and forecasters will report on it's location. In their reports it might show it's location and known wind speed at a particular time. This is a fairly straightforward example of using temporal data to show where the cyclone has been and where and when it is forecasted to go.
Affected population - over the initial days of a disaster as responders seek to understand the situation there will be reports of numbers of affected people for different locations. These are like to go up and down over time as the information becomes clearer.
Health caseloads - in a epidemic or pandemic the number of people who have been recorded as to having the thing that has caused the epidemic will change.
Market prices - the price of a commodity at a market will go up and down over time depending on the demand for it.
There are a number of things you should do with temporal data in addition to the recommendations in the receiving data section:
When did you receive it - record when you received the data.
Check to see when it was collated and for how long is it valid for - this will give the data some currency.
Check the frequency of the data collected - is it in hours, days, weeks, months, years, etc. In some cases, this could be recorded as day 1, day 2, day 3, etc or in some health emergency epiweek 1, epiweek 2, etc - check when this period start.
The key thing with temporal data is that you should be looking for changes, patterns and trends to the data over time. Things to understand are:
Changes - this is a comparison between two datasets. Calculate what the change is in the number. You may wish to use absolute numbers and/ or as a percentage. What you are trying to identify is if there an increase, decrease or no change at all.
Patterns and cycles - look for patterns in the data and see if you can attribute reasons as to why there is a recurring pattern. For example in a epidemic, it might be observed over time that the daily number of cases might be lower on Monday. This might be attributed to the lower number of testing that occurs over a weekend. In agriculture, crops grow in different seasons and so it can be expected to only see certain foods in the market around the harvest time for that crop.
Trends - this is a longer term view of the data and requires a number of datasets over time to begin to understand what might be happening over time. For example, an area might find that the traditional seasonal rains have started to arrive earlier or later, with more or less intensity. You might consider various statistical methods such as using a moving average to smooth out short term fluctuations.
There are a number of types of visualisation tools and techniques that you can consider using to represent your temporal data.
These are a very useful method of visualising data over time and can help the user see patterns and trends. The temporal element should be used on one of the axis. Traditional examples include histograms, line and gantt charts but there are other examples such as sparklines, rose charts or a heat map that you may wish to consider when looking at changes, patterns or trends. Calendars, timelines and timetables are good ways to show a sequence over a period of time. Further examples can be found here - the data visualisation catalogue.
Maps or likely a series of maps can be used to represent temporal data along side the spatial element. Consider using symbols like up and down arrows to show the change. You may wish to include a series of maps in the same page layout. For example, if you wanted to show monthly rainfall, you may include a map frame for each month in the same page layout.
For maps that include forecasted data it is recommended to differentiate between forecasted and actual measurements. In the example below you will see this is done using solid (actual) and dashed (forecasted) lines. Likewise, it is good practice to label point data with a time or date stamp.
Short animations can be a useful and more appealing way of showing changes over time. Examples of this might be showing the movement of a cyclone along it's track and see when the most intense parts occurred. An animation might require more time to prepare depending on the software and technical skills available.
Any visual tool that allows the user to interact with the data can be beneficial and where temporal data is available a slider should be included. A swipe map, whereby the user can swipe between two images can be an effective way of comparing before and after the event.
A really simple way of showing time is to time or datestamp your products to show when it was produced or when the data is from. You can achieve this by including a time or date in the title, or in the metadata fields of the map or data. This is particularly the case when generating new versions of the product. It is even more important if there is a rapid turnover and frequency of updates such as in a search and rescue response.
