Import predefined session. Choosing any of the predefined sessions will import it into your browser. Later, you can load it on your map.
Import session from a local file. You can import your own or shared session from your local disk. Later, you can load it on your map.
Create a new session. Create a new session, which will store current state of the interactive map application. Useful to save your progress or share your work with other.
Imported sessions. Here are all imported sessions either from your local disk or from our remote resources. You can load them on your map by clicking play_arrow icon. Green label indicates which session is currently loaded.
User dataset
Import dataset from a local file. Here you can browse and load your own dataset formatted as JSON or CSV file. You can read more about possible content of the user dataset file here .
Database queries
Choose dataset. Choose a dataset stored in our graph database. The dataset can be later filtered using the query buidler.
Query builder options. To import a specific dataset, you can define a set of its filters. Filters take the form of nested logical statements.
Database query content. This textbox contains the query in a JSON format. You can also paste here shared query JSON to import the same dataset someone other has used.
Loaded datasets
Sampling.
Random sampling helps to reduce number of visualized objects in case of big datasets.
Samples shuffling can affect results of dynamic clustering by distance and could make it more appealing.
Dataset query. This is query in a JSON format, that was used to fetch the dataset from the database. You can copy it and share it with other or place it in a publication.
Map management
Map grouping
Spatial clustering
Spatial clustering modes. Distance clustering offers dynamic overview of data distribution. Region clustering is conservative. Both can be combined to analyze regional localities.
Cluster appearance. Cluster boundary approximates it's area. Because of the map projection, the boundary's area and position might be different than in reality on a globe.
Clustering range. Affects cluster area. Relative distance may change the contents of clusters on map zoom. Absolute distance is conservative.
Cluster object position. It determines position at which the cluster should be drawn. Cluster origin is it's first sample's position, while cluster centroid is calculated from all cluster's samples positions. This option affects distance between clusters drawings and may improve readability.
Attributes clustering
Choose an attribute. Choose an attribute which options you want to change. You can purge all created groups by using the reset button.
Values & groups browser . Here you can browse either ungrouped values or created groups. Use radio buttons to switch between views.
Add values to group. Here you can either create a new group or add more values to an existing group by providing it's name. The values to add must be selected in the browser.
Manage groups. Modify group option lets you exclude attribute's values from a chosen group. You can do it by double-clicking the value in the browser. The group_work icon means that value is included to the group.
Groups appearance. Define prefix which will be added to every name of automaticaly created groups.
Group by a tree level. Assign attribute's values to groups named after values' parents in the data tree at a given tree level. If a value does not have a parent at the given tree level, it remains ungrouped.
Group by root word of length. Assign attribute's values to groups named after values' word roots. The word roots are found by taking prefixes of the values.
Map filtering
Time filter
Set time filter. Set time filter's interval. Only objects which dating overlaps the filter's interval will be shown on the map.
Set time filter's range. Set time filter's minimum and maximum range. It helps to focus on a particular time period for which there are more samples.
Filtering options.
Choose whether to show or hide contemporary objects.
Choose whether to apply time filter to the regions.
Visibility fading functio. Object visibility fading function determines transparency of the sample clusters when filtering them in time. Transparency factor of a single sample corresponds to the definite integral of it's dating probability distribution function bounded by the time filter interval. Transparency of the samples' cluster is an average of each calculated sample's transparency factor. Available functions include:
Gaussian fading function assumes gaussian distribution of the sample's dating probability
Rectangular fading function assumes continuous uniform distribution of the sample's dating probability
Optimization options. Choose whether to trigger time filtering event only when you release the timeline interval or handle. For big datasets it helps to save a lot of time.
Attribute filter
Choose an attribute to filter. Choose an attribute which options you want to change. You can also trigger the filtering for the chosen attribute. The objects are filtered by all the active filters collectively.
Values browser. Here you can browse chosen attribute's values. Trigger their filtering by double clicking their names or by using the control buttons.
Add values to group. Filter attribute's values by their position in the data tree. The position is defined as the tree level or depth.
Filter by a tree level. Filter objects by their admixture components values. You can define constraints independently for each component.
Region filter
Choose a region type to filter. Choose a region type which options you want to change. The objects clustered by regions will be filtered together with the corresponding regions.
Filter regions. Here you can browse chosen type's regions. Trigger their filtering by double clicking their names or by using the control buttons.
Created map layers
General options
Layer source. Choose one of the loaded datasets to display in on the map. Dataset's objects appearance will be determined by the layers settings.
Displayed attribute. Choose which attribute of the dataset's objects should be display on the map. Objects of different databases may have different attributes.
Layer type.
Single value representations enable to compare different groups of objects by their quantities.
Multiple values representations enable additionaly to compare different groups of objects by their composition.
Map layer options
Piechart
Labels appearance. Change appearance of the attribute's values labels drawn around the piechart. E.g. you can determine whether the labels should be drawn always, never or on specific action.
Tags cloud
Tag cloud appearance. Change appeareance of the tags cloud. E.g. adding comma to the end of the layer's attribute values may improve readability for dense clouds.
Tags placement
Tags strategy placement determines a shape of the path on which subsequent tags are placed in the cloud.
Placement step determines the distance between two subsequent tags. You can try to change this parameter, if some of the tags are overlapping and you want to keep the cloud density.
Padding size determines density of the cloud. The bigger the padding, the more free space is saved around the tags.
Points
Points appearance. Change appearance of the point's body. By disabling the body drawing and setting the weight to be represented by value, you can make the layer display only count numbers. Useful as an overlay for heatmap.
Heatmap
Heatmap radius. Set radius of the heatmap.
Heatmap blurSet blur of the heatmap.
Layer appearance
Layer name. Choose whether to show layer's name below every object displayed by this layer.
Layer color. Set unique color of the feature to make it distinguishable on the map. Layer blending option defines how layer objects should blend with colors of the other objects and map underneath. It's useful for visualization of heatmaps overlap.
Layer pointer. Options defining appearance of the layer's objects' pointers, which link graphical representation of the objects with their spatial origin.
Feature position
Offset angle. Set an angle at which layer's objects' representations are placed in relation to their spatial origin.
Offset position ratio. Set an offset distance at which layer's objects' representations are placed in relation to their spatial origin. You can choose if the offset value should be relative to the cluster's size.
Feature size
Size range. Size range determinines minimum and maximum size of the displayed objects and clusters. The cluster size can be altered by data weighting settings. If weighting is disabled, the max value of the range is taken as the objects size.
Font size ratio. Adjust font size ratio to fit big numbers into the object. Font size is also relative to the object's size.
Data weight
Weight appearance. Determine how the data weight information should be presented.
Displayed value type. Choose whether the displayed weight value should be the weight itself or the count of the cluster, that is being weighted.
Weight function. Weight function determines the dynamic of the weight change. Useful when comparing datasets of different sizes. You should fit weight function to the counts normalization type.
Count normalization method. Counts normalization method can transform raw counts into fractions, which might be better to utilize by certain weight functions. The counts are normalized only in relation to the size of layer's dataset.
Weight growth factor. Set weighting function growth factor. It determines the steepness of the function.
Count scaling factor. Set counts scaling factor. It scales the raw counts, which may be useful in comparsion of datasets of different sizes.
Other grouping
Group below frequency. Values below given frequency in the objects cluster will be included into OTHER group. This group can be filtered, so you can filter attribute values basing on their frequency.
Group below count. Values below given count in the objects cluster will be included into OTHER group. This group can be filtered, so you can filter attribute values basing on their count.
Features filtering
Values browser. Here you can browse layer's attribute's values. Trigger their filtering by double clicking their names or by using the control buttons. Layer's attribute filtering works independently to global attributes filtering. It's useful to emphasize different parts of the same dataset using different layers.