Location Tracker

Reactive programming with Kotlin Flow offers a powerful approach to handling real-time data, especially useful in applications that require location tracking. The use of StateFlows and operators such as flatMapLatest allows you to create robust observability systems that respond dynamically to changes in application state, while maintaining precise control over data flows.

How to implement a location tracking system with Kotlin Flow?

Location tracking in mobile applications requires efficient handling of real-time data streams. Using Kotlin Flow, we can create a system that observes the location of the device and records this data along with temporal information.

To begin, we need to create a variable that represents the current location and depends on the observation state:

val currentLocation = isObservingLocation.flatMapLatest { isObserving -> if (isObserving) { locationObserver.observeLocation(intervalMillis = 1000) } else { emptyFlow() } }.stateIn( scope = applicationScope, started = SharingStarted.Lazily, initialValue = null)

In this code, we are using flatMapLatest, which works as a channel selector. Depending on the value of isObservingLocation, we switch between observing the location with one-second intervals or stopping the data stream completely. The stateIn operator ensures that this information persists throughout the lifecycle of the application, starting the flow only when some visual component subscribes to the variable.

How to manage tracking and pauses in data collection?

Precise control over when to collect location data is critical to create a smooth user experience and to optimize resource usage. For this, we implement an isTracking variable to help us manage this behavior:

val isTracking = MutableStateFlow(false)
init { isTracking .distinctUntilChanged() .onEach { isTracking -> if (!isTracking) { val newList = buildList { addAll(locationData.value) add(emptyList()) } locationData.update { newList } } } .flatMapLatest { isTracking -> if (isTracking) { tickerFlow(1000) } else { emptyFlow() } } } .scan(0L) { acc, _ -> acc + 1 } .onEach { elapsedTime.update { it } } .launchIn(applicationScope)}

This code performs several important operations:

1. When the tracking state changes to paused, we accumulate the previous list of locations and create a new empty list.
2. We use isTracking to decide whether to emit time values or pause the flow.
3. At each time broadcast, we accumulate that time in the variable elapsedTime
4. The launchIn operator ensures that this stream survives for the entire lifecycle of the application

How to combine location data with temporal information?

Combining location data with timestamps allows us to perform more complex analysis, such as calculating distances and speeds. To achieve this, we create a flow that processes each location broadcast:

currentLocation .filterNotNull() .combine(isTracking) { location, isTracking -> if (isTracking) location else null } .filterNotNull() .zip(elapsedTime) { location, time -> LocationWithTimestamp(location, time) } .onEach { locationWithTime -> val lastLocations = locationData.value if (lastLocations.isNotEmpty()) { val updatedSegment = lastLocations.last() + locationWithTime val updatedList = lastLocations.dropLast(1) + updatedSegment locationData.update { updatedList } } else { locationData.update { listOf(listOf(listOf(locationWithTime))) } } }        
 // Calculate total distance val distanceMeters = calculateTotalDistance(locationData.value) totalDistance.update { distanceMeters } } .launchIn(applicationScope)

This flow is the heart of our location tracking application:

1. We filter out null location values.
2. We combine the location with the tracking state to emit only when we are in active mode
3. We use zip to link each location to its corresponding timestamp
4. Update our list of location segments, adding the new point to the current segment
5. We calculate the total distance traveled and update the corresponding variable

The data structure we use (a list of lists) allows us to keep separate segments when the user pauses and resumes tracking, making it easy to visualize discontinuous routes in the user interface.

Reactive programming with Kotlin Flow provides us with powerful tools to handle complex data flows elegantly and efficiently. By implementing a location tracking system, we can leverage operators like flatMapLatest, combine and zip to create a smooth and responsive user experience. Have you ever implemented similar systems? Share your experience in the comments.