Tygron Query Language
TQL is short for Tygron Query Language. It provides a means of obtaining and adjusting data of a session. It is comprised of a number of key phrases, divided into 2 categories: statements and clauses. The statements indicate what data interaction should take place; retrieving using a SELECT statement, or adjusting using an UPDATE statement. The clauses provide a means to filter the data the interaction is applied to. A query always consists of exactly 1 statement and 0 or more clauses.
Interactions can take place with the data in 2 ways: as item data or polygon data. Item data pertains to a specific component of a project, such as individual buildings, indicators, or globals. Polygon data pertains to geographic data; surface areas in the project area, such as the footprint of buildings, the surface area of neighborhoods, or calculated grids. Depending on what kind of statement is used, the query will interact with its clauses treating them as items or as polygons.
An example of a TQL query is SELECT_LOTSIZE_WHERE_CATEGORY_IS_SOCIAL_AND_STAKEHOLDERTYPE_IS_MUNICIPALITY. In this example, SELECT_LOTSIZE is the statement. CATEGORY_IS_SOCIAL is the first clause, and STAKEHOLDERTYPE_IS_MUNICIPALITY is the second clause. It would return all the land area taken up with social housing, which is also owned by a municipal stakeholder.
Note that all clauses in a TQL query are cumulative. All clauses are connected by the term AND. This means all clauses must be met before the data is "counted". Depending on how you use TQL in your project or applications, it is possible to simulate an "OR" functionality as well (i.e. CATEGORY_IS_SOCIAL or CATEGORY_IS_NORMAL). This can be done simply by executing multiple queries, one for each "OR" section.
More examples can be found in the example section.
Usage
TQL can be used in a number of places. The most common place is in the excels of custom indicators or panels. To indicate which cells should contain certain data from the session, cells in an excel file can be given a name. In this case, the name would be a TQL query. When an indicator calculation takes place, the Tygron Platform will obtain all queries that exist in the excel file of the indicator. For the queries with SELECT statements, the results of those queries are obtained and placed in the corresponding cells. when the calculation of the excel is complete, the cells with an UPDATE statement are read out, and the items in the project indicated by those queries are updated with those values.
TQL can also be used to quickly check data in a project. When in editor mode, it's possible to open the query tool. With this tool, queries can be made and directly executed. The result is then presented to the user. This can be especially handy when it's necessary to quickly check whether a certain type of data is present or correctly readable. The query tool also functions both in and out of test runs, allowing a user to also check data in a project during a session.
It's also possible to execute TQL queries directly via the API. The advantage of using TQL, rather than inspecting a project's data via the API directly, is that it can be used to perform calculations that require some intermediate processing. For example, calculating the intersection of buildings with an area or neighborhood. Other examples of this include the queries related to values of grids, or the multiplication of floor size with a related function value.
Clauses
Clauses indicate the conditions which must be met for any data to be taken into account for a statement. In a query, they are connected to the statement using the phrase WHERE. Multiple additonal clauses can be added to a query using the phrase AND. Clauses added to a query are cumulative conditions which must all be met. When, for any data, at least one condition is not met, that data is not taken into account or affected by the query.
Clauses can be broadly grouped into 3 different groups: Common clauses, singular clauses, and modifying clauses. For any given statement, different clauses can be applied from across all these groups. The availability and function of clauses is also influenced by whether the statement dictates an interaction with polygon data or with item data. Some clauses may or may not be available at all, depending on this.
The order in which the clauses appear in the query is not relevant.
Examples for common use-cases
A number of use-cases come up frequently across various projects. Queries of the following forms often provide a firm basis for using TQL to obtain required data.
Function values (selecting)
Parking requirements of social housing
The parking demand is based on a function value of each function, and is defined as "parking places per m2". Parking demand is not based on the footprint of a building, but the size of a building. i.e. a building 4 floors high needs more parking than a building with the same footprint 2 floors high.
SELECT_FLOORSIZE_WHERE_CATEGORY_IS_SOCIAL_AND_CATEGORYMULT_IS_PARKING_LOTS_DEMAND_PER_M2
SELECT_FLOORSIZE: What we are basically looking for is the floorsize of the building.CATEGORY_IS_SOCIAL: We only want to look at buildings which are of the category social housing. For that, a category clause is added.CATEGORYMULT_IS_PARKING_LOTS_DEMAND_PER_M2: Lastly, we are not looking directly for the floorsize, but for the parking demand as a result of that floorsize.
Function values (updating)
Traffic intensity on roads
The traffic intensity is a function value which affects calculations related to traffic. Some use-cases require these values to be updated dynamically. This can be done per neighborhood. (In this example, the neighborhood in question has the ID 1.)
UPDATE_BUILDING_TRAFFIC_FLOW_WHERE_CATEGORY_IS_ROAD_AND_NEIGHBORHOOD_IS_1
UPDATE_BUILDING: We're changing an attribute of a building.TRAFFIC_FLOW: The attribute we are changing is the TRAFFIC_FLOW, which is the technical name of a function value.CATEGORY_IS_ROAD: We only want to update this value on roads.NEIGHBORHOOD_IS_1: We are updating only in this specific neighborhood. Nothing outside the neighborhood is updated by this statement.
Grid average
Quality of livability in a neighborhood
The livability overlay calculates, for each grid cell, the average livability. The best indication of how well a certain region performs in terms of livability would be to average these values out. That way, its possible to compare places which are not the same size. (In this example, the neighborhood in question has the ID 1, and the grid of livability has the ID 4.)
SELECT_GRIDAVG_WHERE_NEIGHBORHOOD_IS_1_AND_GRID_IS_4
For any given neighborhood, different points of a grid may score differently.
SELECT_GRIDAVG: We're looking for the average score across the entire neighborhood.NEIGHBORHOOD_IS_1: We don't need the average of the entire map, but only of a specific neighborhood.GRID_IS_4: We need to specify the grid we wish to use the values of.
Grid values
The area of open water which is flooded by more than 20 cm of water
The rainfall overlay calculates what amounts of water ends up where. The overlay's results are in meters. If we wish to provide some minimum value, it must be in meters as well. 20 cm is 0,2 meters. The project can also contain multiple types of water. All types of have one thing in common, namely that they all have the WATER attribute, which can be tested against. (In this example, the grid of inundation has the ID 4, and has its result type set to water stress.)
SELECT_LANDSIZE_WHERE_MINGRIDVALUE_IS_0.2_AND_GRID_IS_4_AND_TERRAIN_WITH_ATTRIBUTE_IS_WATER
SELECT_LANDSIZE: The end result must be some area; some amount of square meters.MINGRIDVALUE_IS_0.2: We only want to count the area where the value calculated is at least 0.2 (meters).GRID_IS_4: We need to specify the grid we wish to use the values of.TERRAIN_WITH_ATTRIBUTE_IS_WATER: We are only looking for the water which ends up on water, identified by the "WATER" attribute.
Buffers
The amount of green in or near a neighborhood
Green buildings can be found by their green function value. The value is the amount of green in square meters, per square meter of building. Green can be in a neighborhood, but can also be within a certain range near a neighborhood (say, 20 meters) and still have an effective presence.
SELECT_LOTSIZE_WHERE_NEIGHBORHOOD_IS_1_AND_BUFFER_IS_20.0_AND_FUNCTIONMULT_IS_GREEN_M2
SELECT_LOTSIZE: The end result must be area taken up by buildings; some amount in square meters.NEIGHBORHOOD_IS_1: We want the greenery in a particular neighborhood.BUFFER_IS_20.0: We don't just want to look at buildings in the neighborhood itself, but also buildings within 20 meters of the neighborhoodFUNCTIONMULT_IS_GREEN_M2:The end result must be multiplied by the green value of the respective buildings found.
