LQF Manual

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LQF provides a method to calculate the anthropogenic heat flux. It uses wide-area energy consumption and vehicle ownership values, and uses and higher-resolution residential population data estimate the heat flux from buildings, transport and human metabolism at 60 minute intervals at the spatial resolution of the population data.

  • Energy and vehicle use are assumed to be correlated to residential (night-time) populations
  • Temporal resolution is maximised by applying empirically measured diurnal, day-of-week and seasonal variations to the data.

Workflow to model QF

  1. Select parameters and data sources files
  2. Select output path: This contains model outputs, logs and any pre-processed data that is produced
  3. Perform pre-processing of the data or select existing pre-processed data: A single set of processed input data can be re-used in subsequent runs
  4. Optionally: Specify land cover fractions at high spatial resolution: Allows the spatial resolution of the modelled outputs to be enhanced
  5. Run the model: Executes the model for the chosen date range components.
  6. Visualise outputs: A simple tool is provided to generate maps and time series from the model outputs.

Main user interface

The main user interface allows the user to select the temporal extent of the model run, and the configuration files. The configuration files describe model assumptions and the library of available data files.

Running the model

LQF main dialogue box

1: Specify model configuration files and output path:

  • LQF needs spatial and temporal information about the population, energy consumption and transport in order to model QF at high temporal and spatial resolution:
  • Model parameters file: Fortran-90 namelist file containing numerical parameters required in model calculations
  • Data sources file: Fortran-90 namelist file that contains the locations of spatial and temporal input files used by the model
  • Output Path: Directory into which model outputs and associated data will be stored. Any existing files will be overwritten.

2: Input data is pre-processed:

  • Before it can be used in the model, the wide-area energy use, vehicle ownership and population data in the data sources file must be (dis)aggregated using local population data to match the chosen output areas.
  • Data is processed using the Prepare input data using data sources button. This performs disaggregation and saves the output files to the /DownscaledData/ subfolder of the chosen model output directory. This step can take up to several hours, during which QGIS will not respond to input.
  • If processed input data already exists elsewhere it can be used instead by specifying the path using the Available at: box. The processed files are copied to the /DownscaledData/ subfolder of the chosen model output directory.
  • Optional: Extra disaggregation uses an additional set of inputs so the data can be disaggregated to a higher spatial resolution:
    • Land cover fractions: Land cover fractions calculated using the UMEP land cover classifier in the pre-processing toolbox.
    • Corresponding polygon grid: The ESRI shapefile grid of polygons represented by the land cover fractions.
    • Grid cell ID field: The field of the polygon grid shapefile that contains a unique identifier for each cell. This is used to cross-reference model outputs.

3: Choose temporal domain:

  • Dates to model (outputs are produced at 60-minute intervals). Either:
    • Date range: The first and final dates are specified and the whole period is simulated.
    • Date list: A comma-separated list of dates in YYYY-mm-dd format (e.g. 2015-01-02, 2016-03-05, 2014-05-03) is provided. These dates are simulated in their entirety.

4: Run model and visualise results:

  • The Run Model button executes the model, which applies the temporal disaggregations and calculates QF components in each output area. This takes up to several hours for high resolution or long study periods. During this time QGIS will not respond to input.
  • Results are visualised using the Visualise... button
  • Previous model results are retrieved using the Load Results button, which allows a previous model output folder to be selected.

Data are ready for use in QF calculations after this point

LQF results visualisation dialogue box
Time series output example

Visualising output

A simple visualisation tool accompanies the model which produces maps and time series plots of the available data.

Time series plots

  • One plot per output area is produced for all of the time steps present in the model output directory, showing the three QF components on separate axes. To plot a time series, select the output area of interest and click the "Show plot" button. The plot areas can be manipulated and graphs exported using the tools in the plot window.


  • One map per QF component and time step is produced, coloured on a logarithmic scale according to the QF value in each output area. One or more LQF time steps is selected in the list, and every QF component is displayed for each date in the QGIS window by pressing "Add to canvas".

Note: Rendering maps may take several minutes for high-resolution model results.

Model outputs

Model outputs are stored in the /ModelOutput/ subdirectory of the selected model output directory. A separate data file is produced for each time step of the model run. Each file contains four columns (one each for total, building, transport and metabolism) and a row for each output area.

  • Output files are timestamped with the pattern LQFYYYYmmdd_HH-MM.csv, with times stated in UTC.
    • YYYY: 4-digit year
    • mm: 2-digit month
    • dd: 2-digit day of month
    • HH: 2-digit hour (00 to 23)
    • MM: 2-digit minute
  • The first model output is labelled 01:00UTC and covers the period 00:00-01:00 UTC.
  • Each data file is in comma-separated value (CSV) format

Synthesised shapefiles

If pre-processing of the input data has taken place, the Disaggregated energy, transport and population shapefiles are stored in the /DownscaledData/ subdirectory of the model outputs, with filenames that reflect the time period they represent. This folder can be used as the source of processed input data in future runs to save time, provided that the data sources file has not changed.

If previously processed input data are being used, these are copied to the /DownscaledData/ subdirectory of the current model run


Several log files are saved in the /Logs/ subdirectory. The logs are intended to help interpretation of model outputs by providing a traceable history of why a particular spatial or temporal disaggregation value was looked up.

  1. The steps taken to disaggregate spatial data, including which attributes were involved
  2. The day of week and the time of day that was returned from each diurnal and annual profile data source when it was queried with a particular model time step.

Configuration files

The Parameters and Data Sources file are copied to the /ConfigFiles/ subdirectory of the model output directory for future reference.

Input data

Input data consists of spatial and temporal information, a lookup table for vehicle fuel efficiency and (optionally) land use cover data to further enhance the spatial resolution of the model output.

Spatial information

Wide-area data

An internal database contains nation-level parameters. These are disaggregated and downscaled based on residential population data. Any output areas spatially outside a territory will be labelled as belonging to no nation, and therefore receive zero vehicles, energy consumption or metabolism.

The database contains the following data for each country. Some of these are time varying, which values stored for each year that data is available (1950 onwards). The data can be added to using standard SQL tools such as SQLite browser, the pandas package in Python or open-source programming tools. Data can be added for any or all time-varying quantities, and non-consecutive years are permitted. The entries are as follows:

Attribute Description Units
kwh_year Total annual primary energy consumption (time-varying) kWh per year
motorcycles Total motorcycle ownership (time varying) Per 1,000 people
cars Total passenger car ownership (time varying) Per 1,000 people
freight Total freight vehicles (time varying) Per 1,000 people
ecostatus World Bank national income classification (1 to 4, 1 being highest) -
summer_cooling Whether summer cooling is a significant impact on energy consumption (1=Yes, 0=No) -
wake_hour Time when 50% of the population has woken up in the morning Hour of day (local time)
sleep_hour Time when 50% of the population has gone to sleep at night Hour of day (local time)
transition_time Timescale over which waking and sleeping occurs Hours
population Total population (time-varying) -
fixedHolidays Days of the year that contain fixed public holidays for each country (e.g. December 25 in the UK) DOY (non-leap year. Adjusted values used when leap year modelled)
weekendDays The days of the week that are assumed as weekends in each country 1 (weekend) or 0 (weekday)
weekendCycles Country-specific diurnal variation for weekend building energy consumption and traffic flow Local time
weekdayCycles Country-specific diurnal variation for weekday building energy consumption and traffic flow Local time

Time indexing of wide-area data

The model selects an appropriate time-varying value (e.g. population) from the database as follows:

  1. If the model time step is before the first available year, the model will report an error.
  2. If the model time step is after the final available year, the latest value is used.
  3. If the model time step is in between two available years, the earlier year is used.

Local data

An ESRI shapefile containing spatially resolved population data. This is used to disaggregate the wide-area totals and estimate metabolism across the study area.

  • Since population data are key to the model method, it is important to use the finest available spatial scale.
  • The model must output results for a consistent set of spatial units, so the populations are assigned to the model output areas based on how much each spatial unit of population is intersected each output area. It is recommended that a population shapefile is chosen as the output areas.
  • The field containing the population must be labelled "Pop" in the shapefile attributes

Temporal information

Information needed by LQF

Temporal data allows the annualised data provided by the shapefiles to be temporally disaggreated into time series. LQF requires daily and hourly information:

  1. Daily information: The mean daily temperature (degrees Celsius) for the region being studied, covering the period of study. The model estimates day-to-day changes in building energy consumption based on the daily mean temperature. The temperature input file for each year is provided by a file with 365 (or 366) entries.
  2. Hourly information: Template diurnal cycles at 60-minute intervals for total energy consumption, total traffic flow, metabolic heat emitted per person and the proportion of the population emitting this heat.
    • Variations of these cycles for different days of week
    • Variations of the above at different times of year (if available)
  3. Time zone information: Temporal files must contain the time zone represented by the data file. Time zones are specified using the list of [standard time zone names.].

Metabolism is based purely on data in the LQF database and can't be overridden. The LQF database contains one default diurnal profile for traffic flow and building energy consumption, but these should be overridden with local data files whenever possible:

QF component File description(s) Size of file
Transport Traffic flows for each vehicle type during each day of the week 7 days * 24 hours * N seasons
Building Building energy consumption during each day of the week 7 days * 24 hours * N seasons

Each temporal file contains headers that store metadata used by the model to interpret the data:

  1. The time zone represented by the file (“UTC” or of the style “Europe/London”). If “UTC” is specified, then values must be explicitly provided for each daylight savings regime to capture shifts in human behaviour. Note that the model outputs are always UTC, with the necessary conversion taking place in the software.
  2. The start and end dates of the period represented by the data. This allows seasonality to be captured.

Ideally these files contain data taken from the period being modelled, but this is not always practical. In this case, temporal profile data from the most recent available year is looked up for the same day of week (taking into account public holidays), season and daylight savings regime if applicable. Different variants are used for traffic, energy and metabolism, and each of these is described below.

Details of temporal input files

Daily temperature

This file records daily air temperature, from which the model estimates the response in building energy consumption. These are expressed in degrees Celsius.

The file consists of Two columns. The first is the day of year; the second is the temperature. The file must contain values for the days from StartDate to EndDate (inclusive), and the column and row headers must be identical to those shown.

Data T_Celsius
StartDate 2015-01-01
EndDate 2015-12-31
Timezone Europe/London
1 9.161881378
2 9.582277749
3 5.615161127
4 3.62641677
5 8.310810996
6 8.237201333
7 7.586860408
Diurnal variations

The same file format is used for both traffic flow and building energy consumption. Each file contains 7 days of data at 1 hour resolution (168 rows). The first row represents the period 00:00-01:00 on Monday morning, and the final row represents 23:00-00:00 on Sunday Evening (into Monday).

The following header lines must be present:

  • Season: A name for the period represented by each column.
  • Start Date: The first day of the period (e.g. season) represented by the data
  • End Date: The final day of this period


  • Periods are not allowed to overlap
  • The units of measurement are not important: The values within a given day are normalised after they are loaded into the model software

The example below shows the first 24 rows of a file that contains entries for the 4 quarters of 2014. Any number of seasons/periods of year can be added to a single file, and multiple files can be added.

Season Q1 Q2 Q3 Q4
StartDate 2014-01-01 2014-04-01 2014-07-01 2014-10-20
EndDate 2014-03-31 2014-06-30 2014-09-30 2014-12-31
Timezone Europe/London
01:00 0.273 0.294 0.306 0.287
02:00 0.236 0.248 0.259 0.242
03:00 0.228 0.238 0.24 0.228
04:00 0.219 0.228 0.227 0.222
05:00 0.226 0.226 0.227 0.222
06:00 0.254 0.245 0.238 0.238
07:00 0.355 0.297 0.275 0.304
08:00 0.477 0.395 0.349 0.387
09:00 0.487 0.509 0.48 0.448
10:00 0.473 0.542 0.532 0.456
11:00 0.45 0.51 0.567 0.442
12:00 0.448 0.502 0.576 0.44
13:00 0.458 0.507 0.591 0.439
14:00 0.436 0.487 0.552 0.421
15:00 0.431 0.478 0.539 0.402
16:00 0.468 0.478 0.563 0.417
17:00 0.554 0.533 0.629 0.482
18:00 0.65 0.649 0.698 0.547
19:00 0.723 0.691 0.763 0.569
20:00 0.709 0.665 0.757 0.545
21:00 0.661 0.622 0.685 0.555
22:00 0.593 0.572 0.606 0.548
23:00 0.496 0.488 0.497 0.474
00:00 0.36 0.393 0.358 0.359

Metabolic activity

Metabolic activity is calculated based on the parameters in the database, which do not change over time (unlike energy consumption, population and vehicle ownership).

The populace is assumed to emit more metabolic energy during waking hours than during sleep, with a linear transition between these two states based on the time people generally wake and sleep in each country. A study area spanning national boundaries therefore shows spatial variation in metabolic activity in the morning and evening if the countries have different waking and sleeping hours in the LQF database.

Recycling of temporal data

The model calculates fluxes for any date provided there is temporal data for the corresponding time of year. If daily temperatures and/or diurnal cycles are not available for the date being modelled, a series of lookups is performed on the available temporal data to find a suitable match. This process accounts for changes in public holidays, leap years and changing DST switch dates.

For diurnal cycle data, the lookup operates by building and then reducing a shortlist of cycles that may be suitable:

  1. Based on the modelled time step, cycles from a suitable year are added to the shortlist. A year is deemed suitable if it contains data covering the time of year being modelled
    • If the modelled year is later than available data, the latest suitable year is used
    • If the modelled year is earlier than the available data, the earliest suitable year is used
  2. The modelled day of week is established (set to Sunday if a public holiday)
  3. The lookup date is set as the same day of week, month and time of month as the modelled date, but in the year identified as suitable.
    • This operation sometimes causes late December dates to become early January. Such dates are moved into the final week of December.
  4. The daylight savings time (DST) state is identified for the lookup date, based on the time shift at noon.
  5. Down-select the available cycles based on the DST state:
    • If the cycles are not provided in the local time of the city being modelled, the search is narrowed to those cycles for periods/seasons matching this DST state
    • If the cycles are provided in the local time of the city being modelled, all periods/seasons are available
  6. Remove any cycles that do not contain the necessary day of week from the shortlist
  7. The most recent cycle with respect to the lookup date is used

The same process is used to identify a relevant daily temperature, except in this case a single value is looked up instead of a cycle and each day of the year is its own season to improve resolution.

Further spatial disaggregation

This is optional. It assigns transport, building and metabolism heat fluxes to only those regions of that map with compatible land covers. Since land cover fraction data are often available at high spatial resolution, this increases the resolution of the model outputs beyond the output areas that were specified initially.

Each model output area is divided into a number of "refined output areas" (ROAs). The land cover fraction lists the proportion of each ROA occupied by:

  • Water
  • Paved surfaces
  • Buildings
  • Soil
  • Deciduous Trees
  • Coniferous Trees
  • Grass

The GQF user interface requires two input files for this process.

  • Land cover fractions: Land cover fractions calculated using the UMEP land cover classifier in the pre-processing toolbox.
  • Corresponding polygon grid: The ESRI shapefile grid of polygons represented by the land cover fractions. This is a required input for the UMEP land cover classifier.

Note that this feature may be very slow and memory limitations may cause it to fail or produce very large output files.

The overall building, transport and metabolic QF components in an MOA are attributed to each ROA based on a set of weightings that associate land cover classes with QF components.

A fixed set of weightings determines the behaviour of this routine and ensure the following principles are satisfied:

  1. Transport heat flux only occurs on paved areas (roads)
  2. Building heat flux only occurs where there are buildings
  3. Metabolic energy reflects the distribution of people between indoor and outdoor environments
Land cover class Weightings (columns must sum to 1)
Building 1 0.8 0
Paved 0 0.05 1
Water 0 0.0 0
Soil 0 0.05 0
Grass 0 0.05 0
Deciduous Trees 0 0.0 0
Coniferous Trees 0 0.05 0

Current limitations:

  • Building height not accounted for: same fraction of QF would be assigned to a very tall building and short building if they occupied the same footprint, despite the former being likely to emit more heat per square metre of the surface it occupies
  • Land cover data: assumed to be consistent with the original input data. If non-zero building energy is calculated in an MOA that has a building land cover of zero, then this energy is lost.

Temperature response functions

Built-in response

LQF contains a database of country-specific parameters that link temperature to building energy consumption via heating degree days (and cooling degree days if air conditioning is assumed to be significant in that country). This forms a temperature response function.

In the model, mean daily building energy consumption is estimated by dividing the annual consumption by the number of days in a year. For each modelled day, this figure is multiplied by the temperature response function for that day. This allows the model to estimate seasonal and day-to-day variations in energy consumption and therefore QF. Lindberg et al. (2013) details the response function and how it varies from country to country.

User-defined response

An alternative temperature response function can be used to override the built-in values. This uses 7 parameters, illustrated below: T response.png

  1. Tc: Temperature above which air conditioning is used [°C]
  2. Th: Temperature below which heating is used [°C]
  3. Ac: Coefficient relating temperature above Tc to energy consumption
  4. Ah: Coefficient relating temperature below Th to energy consumption
  5. c: Constant that sets minimum value
  6. Tmin: Temperature below which energy use from heating stops varying [°C]
  7. Tmax: Temperature above which energy use from cooling stops varying [°C]

Despite the direction of the slopes, Ah and Ac are both positive coefficients that act on the absolute difference between T and Th or Tc (respectively).

To activate the custom response function, the parameters must be specified in the parameters file.

Configuration data

The LQFsoftware has two configuration files:

  • Data sources file: Manages the various input data files and their associated metadata
  • Parameters file: Contains numerical values and assumptions used in model calculations.

Parameters file

The LQF parameters file contains public holidays and numeric values used in calculations. The table below describes the entries in each parameters file.

Parameter name Description
params: Model run parameters
  • timezone
The time zone of the modelled area. Expressed in Continent/City format (e.g. Europe/London). [List of valid time zones.].
  • use_uk_holidays
Set to 1 to use UK public holidays (calculated automatically) or 0 otherwise
  • use_custom_holidays
Set to 1 to use a list of public holidays (specified separately) or 0 otherwise
  • custom_holidays
A list of custom public holidays in YYYY-mm-dd format.
  • avgspeed
Mean speed (metres per hour) of traffic
  • emissionfactors
Emissions factors in [W.m-2] for cars, motorcycles and freight vehicles
  • balance_point_temperature
Outdoor air temperature below/above which the building energy is assumed to change as a result of active heading/cooling.
  • balance_point_multfactor
Factor applied to the difference between air temperature and balance point temperature to estimate the building energy response
  • QV_multfactor
Assumed proportion of vehicle fleet in use per day
  • sleep_metab
Assumed metabolic heat emission per person [W] while resting (sleep)
  • work_metab
Assumed metabolic heat emission per person [W] while active (awake)
CustomTemperatureResponse: Optional parameters for a custom temperature response function
  • Th
Daily mean Temperature below which heating is used (celsius)
  • Tc
Daily mean Temperature above which artificial cooling is used (celsius)
  • Ah
Coefficient relating temperature below Th to energy consumption
  • Ac
Coefficient relating temperature above Tc to energy consumption
  • c
Constant that sets minimum value of response function
  • Tmax
Temperature above which energy use is constant with temperature
  • Tmin
Temperature below which energy use is constant with temperature

Values for the land cover weightings discussed above are also included in the parameters file.

Example parameters file (without user-defined temperature response)

A model configuration for the UK, with two more public holidays than are ordinarily present.

   timezone = "Europe/London"
   use_uk_holidays = 1 
   use_custom_holidays = 1 
   custom_holidays = '2016-06-21', '2016-06-22' 
   avgspeed = 48000. 
   emissionfactors = 25.92, 13.16, 108.42 
   balance_point_temperature = 12.
   balance_point_multfactor = 0.7
   QV_multfactor = 0.8
   sleep_metab = 75  
   work_metab = 175  

   ! For optional additional spatial disaggregation, triplets of weightings for land cover classes
   ! Values for [Building, Transport, Metabolism] respectively
   grass           = 0, 0, 0.025
   baresoil        = 0, 0, 0
   paved           = 0, 1, 0.10
   buildings       = 1, 0, 0.85
   water           = 0, 0, 0
   decidioustrees  = 0, 0, 0.025
   evergreentrees  = 0, 0, 0

User-defined temperature response section

To override the built-in temperature response function, the following section must be added to the parameters file (arbitrary values are used here as examples)

	Th = 10
	Tc = 20
	Ah = 0.1
	Ac = 0.2
	c = 0.5
	Tmax = 50
	Tmin = -10

Data sources file

The data sources file manages the library of shapefiles and temporal profile files used by the model. It is divided into a number of sections (described below).

Output areas

The shapefile that defines the model output areas to be used: all input data are disaggregated into these spatial units, and the model results are shown using them. In the simplest case, the same shapefile is used for both outputAreas and Residential population (see below).

There are three entries:

Parameter Description
Shapefile Location of the shapefile on the local machine
epsgCode EPSG code (numeric) of the shapefile coordinate reference system
featureIds Column that contains a unique identifier for each output area (optional: order of the output areas in the file is used if empty). This is used for cross-referencing and is shown in the model outputs.

An example:

   shapefile = 'C:\LQF\PopDens_2014.shp'
   epsgCode = 27700
   featureIds = 'LSOA11CD' 

International database

Nation-level population, vehicle registrations, energy consumption and socio-economic data for multiple years are stored in a Spatialite database file. The location of this file is specified in the data sources file as follows:

   path = 'C:\LQF\InternationalDatabase.sqlite'

Residential population shapefile

Entries for the 'residentialPop' section of the data sources file (residential population data) example:

   shapefiles = 'C:\LQF\popOA2014.shp'
   startDates = '2014-01-01'
   epsgCodes = 27700

Note: The population must appear under the attribute "Pop" in the residential shapefile.

Note that a “startDate” and “epsgCode” must be specified for each shapefile. Providing the incorrect EPSG code will result in incorrect or zero heat fluxes being modelled because the mis-projected model areas never overlap.

Temporal data: Metabolism, energy use and transportation temporal profiles

Air temperature (required)

Daily mean temperature (in the local time zone of the location being studied) is a required input. Data can be provided for multiple years using a comma-separated list of files.

Energy consumption and traffic flow profiles (optional)

The LQF database contains default diurnal profiles for traffic and building energy consumption, and this varies if the study area overlaps countries with different profiles. These profiles are overridden if user-specified data are supplied instead, and the user-specified values are applied to the entire study area.

An example that provides all three temporal data sources is shown below, and two years of data are provided for air temperature.

   ! Mean daily air temperature data
   dailyTemperature = 'C:\LQF\dailyT_2013.csv', 'C:\LQF\dailyT_2014.csv'

   ! Diurnal profiles
   ! Omit entries to use default LQF database values
   diurnEnergy = 'C:\LQF\buildingProfiles.csv'
   diurnTraffic = 'C:\LQF\transportProfiles.csv'

Using multiple temporal profile files

As with shapefiles, multiple temporal profile files can be loaded into the model to capture different periods of time. All of the data is combined into a single file inside the model, provided that none of the periods described within the files clash.

Example data sources file

A complete data sources file appears as follows. Note that two data files are specified for the daily temperature data so that a longer time series can be modelled.

! ### Model output polygons
   shapefile = 'C:\LQF\population.shp'
   epsgCode = 32631
   featureIds = 'ID' ! The attribute to use as a unique ID for each areas (optional; for cross-referencing)

! ### Residential population data for the city being studied
! Must contain total population in each area under the attribute "Pop"
   shapefiles = 'C:\LQF\population.shp'
   startDates = '2014-01-01'
   epsgCodes = 32631
   featureIds = 'ID'

   path = 'C:\LQF\InternationalDatabase.sqlite'

   ! Air temperature each day for a year
   dailyTemperature = 'C:\LQF\temp_2013.csv', 'C:\LQF\temp_2014.csv'
   ! Provide file(s) for building energy consumption and/or traffic flow diurnal cycles
   ! Omit entries to use default LQF database values
   diurnEnergy = 'C:\LQF\buildingProfiles.csv'
   diurnTraffic = 'C:\LQF\transportProfiles.csv'


Appendix A: Converting a population raster to a vector shapefile using QGIS

Global population datasets are generally available as raster files, but LQF requires a set of population counts as vector polygons. This guide explains how to convert a raster dataset to a set of polygons for use in LQF. Examples are shown using a Greater London population count dataset at 250m resolution.

  1. Load the raster file into QGIS
  2. Rename the layer to "Pop" (this saves time later)
  3. Make sure the project coordinate reference system (CRS) is the same as for the raster. To change it, click the label and choose the correct CRS from the list:
  4. Create a vector grid aligned to the raster:
    • Vector -> Research Tools -> Vector Grid
      Fig 1
    • This will show the Vector Grid dialog box:
      • In Grid Extent:
        • Choose "Pop"
        • Click Align extends and resolution to the selected raster layer (unless you want to choose the grid parameters manually to extract a subset of the raster)
        • Click Update extents from layer to fill in the text boxes
          • If this option is not available, you will need to get the resolution of the raster layer by inspecting its metadata (right click the layer > Properties > Metadata > Pixel size)
      • In Parameters:
        • Check Output grid as polygons
        • Choose where to save the resulting shapefile containing the grid
        • Check Add results to canvas so the grid can be used
  5. The raster values must now be extracted from the raster layer into the vector grid. Use the "Add raster values to features" tool from Processing > Toolbox > SAGA > Vector to raster:
    Saga1.png Saga2.png
    • In Parameters, choose:
      • Shapes: The vector grid that you created
      • Grids: Press "..." and select the "Pop" raster layer
      • Interpolation: Nearest neighbour (selects the nearest raster data point)
      • Result: The location of a new shapefile that contains the vector grid and the population in each cell
    • Press "Run". The resulting shapefile will be added to the layers. It contains a "Pop" column for the population
    • Use this shapefile as the residential population in LQF (in the LQF Data sources file)