Abstract: Provided herein is a method for quantifying and measuring human mobility within defined geographic regions and sub-regions. Methods may include: identifying sub-regions within a region; identifying static information associated with the sub-regions from one or more static information sources; obtaining dynamic information associated with the sub-regions from one or more dynamic information sources; determining correlations between elements of the static information associated with a respective sub-region and elements of the dynamic information associated with the respective sub-regions; generating a mobility score for the respective sub-region based, at least in part, on the correlations between the elements of the static information and the elements of the dynamic information associated with the respective sub-region; and providing the mobility score to one or more clients for guiding an action relative to the mobility score.

Abstract: Systems and methods for communicating uncertainty around stationary objects are provided. For example, a method for communicating uncertainty around stationary objects includes receiving sensor data corresponding to a stationary object at a location along a road segment. The sensor data is captured via one or more sensors of a first vehicle. The method also includes based on the sensor data, determining a level of uncertainty corresponding to the stationary object. The method also includes based on the determined level of uncertainty, providing an instruction for one or more sensors of a second vehicle to capture additional sensor data corresponding to the stationary object at the location along the road segment.

Abstract: Provided herein is a method for a framework to predict the population density for an area based on indirect measurements and contextually similar areas. Methods may include: receiving ground truth population data corresponding to a first region; determining map features associated with the first region; receiving dynamic mobility data associated with the first region; training a machine learning model based on the ground truth population data corresponding to the first region, the map features associated with the first region, and the dynamic mobility data associated with the first region; receiving dynamic mobility data associated with a second region; determining map features associated with the second region; processing the dynamic mobility data associated with the second region and the map features associated with the second region using the machine learning model; and receiving, from the machine learning model, a population estimate for the second region.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: Provided herein is a method for quantifying and measuring human mobility within defined geographic regions and sub-regions. Methods may include: identifying sub-regions within a region; identifying static information associated with the sub-regions from one or more static information sources; obtaining dynamic information associated with the sub-regions from one or more dynamic information sources; determining correlations between elements of the static information associated with a respective sub-region and elements of the dynamic information associated with the respective sub-regions; generating a mobility score for the respective sub-region based, at least in part, on the correlations between the elements of the static information and the elements of the dynamic information associated with the respective sub-region; and providing the mobility score to one or more clients for guiding an action relative to the mobility score.

Abstract: An approach is provided for routing an aerial vehicle based on a relative noise impact. The approach, for example, involves retrieving environmental noise map data for a geographic area. The environmental noise map data indicates existing noise levels measured in the geographic area. The approach also involves determining a vehicle noise characteristic of the aerial vehicle. The approach further involves generating a route for the aerial vehicle over the geographic area based on the relative noise impact of the aerial vehicle while operating over the geographic area. The relative noise impact is computed based on the vehicle noise characteristic relative to the existing noise levels of the environmental noise map data for portions of the geographic area under the route of the aerial vehicle.

Abstract: Provided herein is a method for quantifying and measuring human mobility within defined geographic regions and sub-regions. Methods may include: identifying sub-regions within a region; identifying static information associated with the sub-regions from one or more static information sources; obtaining dynamic information associated with the sub-regions from one or more dynamic information sources; determining correlations between elements of the static information associated with a respective sub-region and elements of the dynamic information associated with the respective sub-regions; generating a mobility score for the respective sub-region based, at least in part, on the correlations between the elements of the static information and the elements of the dynamic information associated with the respective sub-region; and providing the mobility score to one or more clients for guiding an action relative to the mobility score.

Abstract: Provided herein is a method for quantifying and measuring human mobility within defined geographic regions and sub-regions. Methods may include: identifying sub-regions within a region; identifying static information associated with the sub-regions from one or more static information sources; obtaining dynamic information associated with the sub-regions from one or more dynamic information sources; determining correlations between elements of the static information associated with a respective sub-region and elements of the dynamic information associated with the respective sub-regions; generating a mobility score for the respective sub-region based, at least in part, on the correlations between the elements of the static information and the elements of the dynamic information associated with the respective sub-region; and providing the mobility score to one or more clients for guiding an action relative to the mobility score.

Abstract: An approach is disclosed for estimating population density change where dynamic signals are not available or are not dense enough to be representative. The approach involves, for example, determining map features of a first map space. The approach also involves identifying partitions of the first map space based on the identified partitions (i) having features that are substantially similar, and (ii) having respective change functions that are substantially similar. The approach further involves determining an estimated change function based on one or more of the respective change functions that are substantially similar and that are associated with the first map space. The approach further involves using the estimated change function for at least one partition of a second map space based on the at least one partition of the second map space and at least one map partition of the first map space having map features that are substantially similar.

Abstract: Provided herein is a method for identifying a probability of observing a predetermined number of people within a geographic area. Methods may include: receiving static population data including a population count associated with each of a plurality of geographical areas of a geographic region, where static population data is updated no more frequently than a predefined frequency; receiving dynamic population data for the geographic region including the plurality of geographical areas, where dynamic population data is updated more frequently than the predefined frequency; associating the dynamic population data with geographic sub-areas of the geographic region and times in which the dynamic population data was received; and calculating, from the static population data and the dynamic population data, a probability of observing a predefined number of people within each geographic sub-area for a specific time.

Abstract: An approach is provided for routing an aerial vehicle based on a relative noise impact. The approach, for example, involves retrieving environmental noise map data for a geographic area. The environmental noise map data indicates existing noise levels measured in the geographic area. The approach also involves determining a vehicle noise characteristic of the aerial vehicle. The approach further involves generating a route for the aerial vehicle over the geographic area based on the relative noise impact of the aerial vehicle while operating over the geographic area. The relative noise impact is computed based on the vehicle noise characteristic relative to the existing noise levels of the environmental noise map data for portions of the geographic area under the route of the aerial vehicle.

Abstract: An approach is provided for computing a three-dimensional route (e.g., a flight path) based on risk-related data. The approach, for example, involves receiving an input specifying an origin, a destination, or a combination thereof for generating a three-dimensional route at a specified time. The approach also involves retrieving risk-related data for one or more candidate three-dimensional routes based on the origin, the destination, or a combination thereof. The risk-related data, for instance, indicates an occurrence of a safety risk to a vehicle (e.g., aerial vehicle) traveling the one or more candidate three-dimensional routes at the specified time. The approach further involves determining the three-dimensional route from the one or more candidate three-dimensional routes based on computing that the risk-related data for the determined three-dimensional route meets a risk threshold.

Abstract: An approach is provided for visualizing risk levels associated with aerial vehicle flights. The approach, for example, involves determining a flight path for an aerial vehicle. The approach also involves calculating at least one risk area along the flight path based on risk-related data associated with the flight path (e.g., data on population density, electromagnetic fields, absence of location signals such as Global Positioning System (GPS) signals, weather, network coverage, aviation related data, etc.). The approach further involves generating a representation of the at least one risk area as a virtual obstacle object and rendering the virtual obstacle object in a user interface of a device in relation to the flight path.