Informed Decision Making Visualisation Methodology Cases Research Data

1_VisualisationCases_AnalysisData Introduction 1 Visualisation Methodology for Informed Decision-making Applied to Smart City and Digital Twin contexts Results of the PoliVisu, DUET and COMPAIR H2020 EU Projects *1 Overview of the research data TITLE Visualisation Methodology for Informed Decision-making Applied to Smart City and Digital Twin contexts VERSION 1.0 RESEARCH MATERIAL IDENTIFIER Research material: 10.5281/zenodo.17379999 Nr. Tab name Title Caption 1 Table 1 Cases TABLE 1: Overview of the smart city-related visualisation cases using the visualisation methodology - 2 Table 2 VisualisationTechniques TABLE 2: Overview of visualisation cases and their potential use in the policy cycle Caption: Smart City Visualisation Cases versus Policy-making Cycle Phases 3 Table 3 VisualisationCases TABLE 3: Quantitative overview of visualisation techniques and their potential use in the policy cycle Caption: Smart City Visualisation Instrument versus Policy-making Cycle Phases 4 Table A1 TABLE RESEARCH DATA 1: Visualisation Methodology - Analysis data Caption: Overview of the initial request, policy elements, visualisation goals, data fields and chosen visualisation techniques and tools (grouped by initial requests) 5 Table A2 TABLE RESEARCH DATA 2: Visualisation Methodology - Steps Caption: Visualisation methodology step-by-step approach (case level) 1_VisualisationCases_AnalysisData Introduction 2 6 Table A3 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use 7 Table 2 & 3 Preparation - Scoring TABLE RESEARCH DATA 4: Policy cycle case mapping Caption: Results of the individual scoring of each case for the policy cycle (sub)phases Nr. File name + document type Title Caption 1 Cases - Visuals (Presentation) Case Visualisations - *1 Policy Development based on Advanced Geospatial Data Analytics and Visualisation (PoliVisu), EU H2020 project, https://cordis.europa.eu/project/id/769608 Digital Urban European Twins for smarter decision making (DUET), EU H2020 project, https://cordis.europa.eu/project/id/870697 Community Observation Measurement & Participation in AIR Science (COMPAIR), EU H2020 project, https://cordis.europa.eu/project/id/101036563 1_VisualisationCases_AnalysisData Table_1_Cases 3 TABLE 1: Overview of the smart city-related visualisation cases using the visualisation methodology Nr. Smart city-related Visualisation Cases (Location & Title) Visualisation Content 1 Berlin (DE), Flanders (BE) - Dynamic exposure visualisation dashboard Dashboard visualising the exposure to fine dust levels (PM2.5) during travel routes on a geospatial map and on graphs visualising levels related to travel distance and exposure time 2 Issy-les-Moulineaux (FR) - Traffic dashboard Traffic dashboard visualising traffic delay, traffic blackspots and free flow speed for Issy-Les-Moulineaux and its surroundings 3 Police zone Voorkempen (BE) - Trajectory speed limit enforcement dashboard Dashboard visualising aggregated average speed control camera data, envisioning live and historical traffic volumes and average speeds 4 Solva region (BE) - Regional traffic behaviour Dashboard visualisation of origin-destination patterns of floating car data on a regional scale in South-Eastern Flanders and its surroundings 5 Herzele (BE) - Interactive school street dashboard Dashboard visualising results from multiple sensor types (traffic, air quality PM, NO2, BC) allowing the comparison of two groups of sensors before and after a moment in time (when the measure has been implemented) - Applied on a school street implementation case 6 Mechelen/Flanders (BE) - Interactive school street dashboard Dashboard visualising traffic count data (cars, big vehicles, cyclists and pedestrians) in and around school streets before and after the implementation of a school street 7 Sint-Niklaas (BE) - Local mobility scheme/plan dashboard Dashboard visualising results from multiple sensor types (traffic, air quality PM, NO2, BC) allowing the comparison of two groups of sensors before and after a moment in time (when the measure has been implemented) - Applied on a local mobility scheme/plan implementation case 8 Flanders (BE) - Interactive road safety map Interactive road safety heatmap for Flanders visualising heatmaps, line and point maps included advanced geo-time and content selection possibilities 9 Ghent (BE) - Student displacements Choropleth map visualising dorm higher education student displacements during a reference period in Ghent by using mobile telecommunication data 10 Pilsen (CZ) - Interactive road accident map Interactive road accident heatmap for Pilsen visualising heatmaps and point maps included advanced geo-time and content selection possibilities 11 Pilsen (CZ) - Interactive sensor based live and historic traffic map Interactive map visualising live and historic traffic volumes in Pilsen, including advanced data geo-time and content selection possibilities 12 Pilsen (CZ) - Traffic measure impact modelling comparison Traffic volume and intensity line delta map of the results of a traffic calculation using the Pilsen traffic model 13 Pilsen (CZ) - Traffic volume impact simulation modelling Traffic volume and intensity line map visualisation of a traffic calculation using the Pilsen traffic model 14 Issy-les-Moulineaux (FR) - Travel planning app Optimal multimodal route calculation mobile visualisation app (My Anatol app) offering sustainable route suggestions 15 Pilsen (CZ) - Impact of roadworks simulation Traffic volume and intensity line map visualisation of the impact of planned roadworks using the Pilsen traffic model 1_VisualisationCases_AnalysisData Table_1_Cases 4 16 Athens (GR) Digital Twin - Green squares planning Visualising the impact of 3D terrain assets (e.g. buildings, constructions, trees, water, street furniture) on the comfort of urban spaces related to liveability, e.g. by avoiding heat stress, using a Digital Twin 17 Athens (GR) Digital Twin - Traffic load & creation of a pedestrian and cycling route Visualising traffic volumes, air quality impact, noise pollution impact (absolute volumes and deltas) in a 3D Digital Twin environment - case transforming roads towards low traffic zones to promote walking and cycling 18 Pilsen (CZ), Ghent (BE) Digital Twin - Impact of road closures Visualising traffic volumes, air quality impact, and noise pollution impact (absolute volumes and deltas) in a 3D Digital Twin environment - case road closures on an existing network 19 Pilsen (CZ) Digital Twin - Ring road construction impact Visualising traffic volumes, air quality impact, noise pollution impact (absolute volumes and deltas) in a 3D Digital Twin environment - case new road infrastructure 20 Pilsen (CZ) Digital Twin - Solar equipment locations in the city park Visualising the impact of 3D terrain assets (e.g. buildings, other constructions, e.g. bridges, trees) on the potential shadow impact on solar panels in a Digital Twin 1_VisualisationCases_AnalysisData Table_2_VisualisationTechniques_PC 5 TABLE 2: Overview of visualisation cases and their potential use in the policy cycle Caption: Smart City Visualisation Cases versus Policy-making Cycle Phases Policy Design Policy Implementation Policy Evaluation Problemsetting Policy formulation Scenario analysis Decision Implementation plan Implementation Ongoing monitoring Communication Impact assessment Problem (re) structur -ing Case Dashboard visualisation Visualisation DB 1 Berlin (DE), Flanders (BE) - Dynamic exposure visualisation dashboard YES - - - - - YES YES - - 2Issy-les-Moulineaux (FR) - Traffic dashboard YES - - - - - - YES YES - 3 Police zone Voorkempen (BE) - Trajectory speed limit enforcement dashboard - YES - - - - YES - YES YES 4Solva region (BE) - Regional traffic behaviour YES YES - - - - - - - - PolicyDB 5Herzele (BE) - Interactive school street dashboard YES - - YES - - YES YES YES - 6Mechelen/Flanders (BE) - School street dashboard YES YES - - YES YES YES YES YES YES 7 Sint-Niklaas (BE) - Local mobility scheme/plan dashboard YES - YES YES YES - YES YES YES YES Intensity map visualisation 8Flanders (BE) - Interactive road safety map YES YES - - YES - - YES YES - 9Ghent (BE) - Student displacements YES YES - - - - - - - - 10 Pilsen (CZ) - Interactive road accident map YES YES - - - - - YES YES - 11 Pilsen (CZ) - Interactive sensor based live and historic traffic map YES - - - - - - YES YES - 12 Pilsen (CZ) - Traffic measure impact modelling comparison YES YES YES YES YES YES YES - YES YES 13 Pilsen (CZ) - Traffic volume impact simulation modelling YES - - - - - - YES - - Algorithm visualisation 14 Issy-les-Moulineaux (FR) - Travel planning app - - - - - - - YES - - 1_VisualisationCases_AnalysisData Table_2_VisualisationTechniques_PC 6 15 Pilsen (CZ) - Impact of roadworks simulation YES YES YES YES YES YES YES YES YES YES Digital Twin visualisation 16 Athens (GR) - Digital Twin, Green squares planning YES - YES YES YES - - YES - - 17 Athens (GR) - Digital Twin, Traffic load & creation of a pedestrian and cycling route YES - - YES YES - - YES YES - 18 Pilsen (CZ), Ghent (BE) - Digital Twin, Impact of road closures YES YES YES YES YES - - YES YES YES 19 Pilsen (CZ) - Digital Twin, Ring road construction impact YES YES YES YES YES - - YES YES YES 20 Pilsen (CZ) - Digital Twin, Solar equipment locations in the city park YES - YES YES YES YES - - YES - ² 1_VisualisationCases_AnalysisData Table_3_VisualisationCases_PC 7 TABLE 3: Quantitative overview of visualisation techniques and their potential use in the policy cycle Caption: Smart City Visualisation Instrument versus Policy-making Cycle Phases Phase Policy Design Policy Implementation Policy Evaluation Subphase Problemsetting Policy formulation Scenario analysis Decision Implementation plan Implementation Ongoing monitoring Communication Impact assessment Problem (re) structur -ing Dashboard visualisation 6/7 3/7 1/7 2/7 2/7 1/7 5/7 5/7 5/7 3/7 -Visualisation DB 3/4 2/4 0/4 0/4 0/4 0/4 2/4 2/4 2/4 1/4 -PolicyDB 3/3 1/3 1/3 2/3 2/3 1/3 3/3 3/3 3/3 2/3 Intensity map visualisation 6/6 4/6 1/6 1/6 2/6 1/6 1/6 4/6 4/6 1/6 Algorithm visualisation 1/2 1/2 1/2 1/2 1/2 1/2 1/2 2/2 1/2 1/2 Digital Twin visualisation 5/5 2/5 4/5 5/5 5/5 1/5 0/5 4/5 4/5 2/5 Percentage / subphase 90% 50% 35% 45% 50% 20% 35% 75% 70% 35% Percentage / phase 55% 45% 53% ² 1_VisualisationCases_AnalysisData Table_A1 VisualisationMethodology_InitialAnalysis 8 TABLE RESEARCH DATA 1: Visualisation Methodology - Analysis data Caption: Overview of the initial request, policy elements, visualisation goals, data fields and chosen visualisation techniques and tools (grouped by initial requests) Case Visualisation link nr. Pilot Initial request Policy Elements Visualisation Goal (related to the request & policy elements) Description of main data fields Main Visualisation Techniques Visualisation Tool Visualisation summary Policy-making cycle (except the communication element) Project 1 - Flanders/Berlin As a citizen, I want to know my exposure to air pollution during a displacement. Policy goal: Supporting environmental awareness Evaluating the impact of air quality, including fine dust (PM) and black carbon (BC), during routes by bike and foot. Output of Air quality sensor, built-in GPS (e.g. Sodaq PM) or Air quality sensor with no GPS, Mobile phone GPS link (e.g. BC meter): - Location X, Y - Time - Exposure PM2.5, PM10 in microgram/m² - Dynamic exposure dashboard interface - 2D Trip visualisation map - Air Quality exposure line chart - Air Quality exposure cumulative line chart - Selection table (trip selection interface) - Selection and filter options Reusable Compair Dynamic Exposure Visualisation dashboard Dashboard including a map visualisation of routes. Route segments have the colour of the air quality measured at the segment. Excposure line chart showing air quality (fine dust particles, black carbon) by trip duration and distance, combined with a cumulative exposure line chart showing the same information. Model simulation of the inhaled dose based on sex, age and activity level. Problem stetting: Dynamic exposure visualisation Compair 1 1.1 Flanders/Berlin Idem Idem Visualise the level of exposure of the route on a city map. Idem - 2D Trip visualisation map Idem 2D Map, segments (measurements intervals + location). Idem Compair 1 1.2 Flanders/Berlin Idem Idem Visualise the level of exposure during the route compared to the WHO health guideline. Idem - Air Quality exposure line chart - Air Quality exposure cumulative line chart Idem Line chart visualising the PM (fine dust in ug/m2), black carbon exposure compared to the WHO guidelines. Idem Compair 1 1.3 Flanders/Berlin Idem Idem Visualise the model output of the inhaled doses during the route. Idem - Air Quality exposure cumulative line chart Idem Cumulative line chart visualising inhaled doses model simulation results in ng/m3. Idem Compair 2 2.1 Issy-les-Moulineaux As a policymaker, I want to know the location and volume of past traffic in the South Paris region. Policy goal: Getting better insights Overviewing traveller time impact by combining road segment-based map views and tables, allowing for comparing and ordering lost time in traffic. Floating car data of wasted time for road segments: - Location (road segment geometry) - Average speed per segment - Lost time "average of extra time spent vs. time without traffic" (quantitative) - Time - Floating car data mobility dashboard - Traffic speed and delay time distribution histogram - Line-based free flow map - Table (lost time) - Free-flow distribution heatmap matrix - Time period selector Custom-built floating car data mobility dashboard for Issy-Les-Moulineaux Dashboard containing multiple visualisations to evaluate traffic-related delays in the Southern Paris region. The dashboard offers a 2D City map view by road segments, flow-line maps to discover (weekly) returning patterns, and tables and heatmaps for comparing two periods in time. Problem-setting, impact assessment: Reduce Congestion PoliVisu 2 2.2 Issy-les-Moulineaux Idem Idem Visualisation of the driving speed and delay time (per hour during the day). Idem -Traffic speed and delay time distribution histogram Idem Visualisation of the driving speed and delay time per hour during the day as a histogram. Idem PoliVisu 2 2.3 Issy-les-Moulineaux Idem Idem Visualise how much the speed decreases on road segments in Issy compared to the normal situation for specific periods and timings. Show current/actual “Time lost” per road segment. Idem - Line-based free flow map Idem Visualisation-based line-based free flow map where the heat of the road segment is defined by the extra time spent due to traffic, versus the theoretical free-flow speed, by showing the speed percentage of free-flow speed (red = slower, green = legal speed, blue = faster) Idem PoliVisu 2 2.4 Issy-les-Moulineaux Idem Idem Visualise a list of moments with the highest "lost times" and a list of road segments ordered by "Total time lost". Idem - Table (lost time) Idem Table summarising the moments with the highest delay times and blackspots. Idem PoliVisu 2 2.5 Issy-les-Moulineaux Idem Idem Visualise the average percentage from free flow during a specific time period for the Issy-Les-Moulineaux area. Idem - Free-flow distribution heatmap matrix Idem Free-flow distribution heatmap matrix depicting the speed percentage from free flow during the day for an entire week, using percentage visualisation combined with colour gradients. Idem PoliVisu 3 - Vlaanderen (Voorkempen) As a police or local community employee responsible for road safety, I want to get better insights into the driver's behaviour (over time) in trajectory control zones. Policy goal: Getting better insights Policy objective: Evaluating if speed control measures are leading to behavioural changes Visualising the moment, quantity and speed of vehicles in the installed trajectory control zones that allow comparison between control zones, timing for policy evaluation and future policy development. - Location speed control zones - ANPR data (pseudonimized), including hashed numberplate data, average speed, time, vehicle type (Raw ANPR data was translated to statistics per trajectory per hour in buckets per speed category) - Average speed control dashboard - Heatmap - Flow rate diagram - Average speed control infraction histogram Reusable ANPR average speed control dashboard Dashboard containing multiple visualisations, including 2D visualisations of trajectory control zones using line segments; a heatmap depicting relative infractions, flow rate diagrams depicting the average speed, number of vehicles and time period; histograms depicting the percentage of infractions, near infractions over time. Policy formulation, monitoring, impact assessment: Identifying trajectory control zones with a high level of speeding infractions. PoliVisu 3 3.1 Vlaanderen (Voorkempen) Idem Idem Visualising the relative percentage of infractions at a typical moment during the week. Idem - Heatmap (time, infraction percentage) Idem Heatmap depicting relative infractions spread over weekdays on an hourly basis. Idem PoliVisu 3 3.2 Vlaanderen (Voorkempen) Idem Idem Visualising the average speed relative to the number of vehicles and time period (during the day). Idem - Flow rate diagram (average speed, number of vehicles) Idem Visualising the average speed relative to the number of vehicles and the time period (morning, day, evening, night). Idem PoliVisu 3 3.3 Vlaanderen (Voorkempen) Idem Idem Visualising the number of infractions and near infractions over time to monitor the long-term evolution of speed behaviour. Idem - Average speed control infraction histogram (infraction percentage, time) Idem Visualising, on average, the speed control zone, the number of infractions and near infractions over time (long-term evolution of speed behaviour). Idem PoliVisu 4 4.1 Vlaanderen (Solva) As a mobility expert, I want an overview of traffic flows within the South-East Flanders region, including both incoming and outgoing traffic. Policy goal: Getting better insights Geospatial visualisation of displacements from one place to another inside the Solva region and from outside to the Solva region. - Locations (local community) - Displacements between and inside a local community (aggregated) - Trip time (aggregated) - Sankey route distribution diagram TomTom Dashboard Sankey route distribution diagram depicting the traffic volumes over a period of time between origin and destination locations (local communities). Problem-setting, policy formulation: Motorised road transport trip distribution. PoliVisu 1_VisualisationCases_AnalysisData Table_A1 VisualisationMethodology_InitialAnalysis 9 TABLE RESEARCH DATA 1: Visualisation Methodology - Analysis data Caption: Overview of the initial request, policy elements, visualisation goals, data fields and chosen visualisation techniques and tools (grouped by initial requests) Case Visualisation link nr. Pilot Initial request Policy Elements Visualisation Goal (related to the request & policy elements) Description of main data fields Main Visualisation Techniques Visualisation Tool Visualisation summary Policy-making cycle (except the communication element) Project 4 4.2 Vlaanderen (Solva) Idem Idem Provide a schematic overview of the most popular origin-destination patterns from the Solva region. Idem - Origin-destination matrix diagram and heatmap Idem Matrix depicting routes (origins) from a selection of places (local communities) toward a selection of places (destinations) using colour gradients to visualise the route volumes. Idem PoliVisu 4 4.3 Vlaanderen (Solva) Idem Idem Geospatial visualisation of the most popular destination from a single origin. Idem - Traffic flow line/trip distribution map Idem 2D map visualising the direction of routes from the origin to the destination local communities in percentages (origin = 100% distributed to multiple destinations). Idem PoliVisu 5 - Vlaanderen (Herzele) As a city official, teacher, and citizen, I want to evaluate the impact of the implementation of a school street at the school and the surrounding streets. Policy goal: Getting better insights Policy action: Implementation of a school street Getting insight into the effects of a school street (test) implementation on traffic volumes and air quality impact. Output of: - Traffic counting sensors (nr of pedestrians, cyclists, cars, big vehicles) - Air quality sensors (NO2, PM, BC) - Location (X, Y) - Policy Monitoring Dashboard for Traffic and Air Quality - 2D Sensor location map - Bar chart (traffic mode changes, changes in pollutant exhaust) Reusable compair policy monitoring dashboard (PMD) A dashboard showing measurements from one group of target sensors and a second group of surrounding sensors before and after a threshold (typically the implementation of a policy measure). The dashboard can present comparable visuals for each sensor type (traffic sensor, air quality sensor). The dashboard supports only fixed sensors. Problem-setting, policy decision, monitoring, impact assessment: Policy Monitoring school streets. Compair 5 5.1 Vlaanderen (Herzele) Idem Idem Geospatial overview of the sensor locations to get an overview of the area. Idem - 2D Sensor location map Idem Visualisation of the location of fixed sensors per group (in this case, school street versus neighbourhood) and per type of sensor, including traffic counting sensors and air quality sensors. Idem Compair 5 5.2 Vlaanderen (Herzele) Idem Idem Statistical overview of the changes in the use of traffic modes and the differences in air quality. Idem - Bar chart (traffic mode changes, changes in pollutant exhaust) Idem Bar chart depicting changes in traffic mode (positive and negative) and air quality pollutants before and after a fixed moment in time (in this case, implementing a school street). Idem Compair 6 6.1 Vlaanderen (Mechelen) As a city official, teacher and citizen, I want an easy-tointerpret data overview to compare traffic volumes before and after the implementation of a school street. Policy goal: Getting better insights Policy strategy: school street as part of a broader mobility policy Policy action: Implementation of a school street Display the difference in distribution over the different traffic modes before and after the introduction of a school street, in the street of the school, as well as in the neighbouring streets. Traffic count data, sensor location data, time data, and weather data - school street implementation dashboard - 2D Map, - Bar chart (advanced) - Comparative trend analysis line chart - Pie chart (modal split) Custom-built school street dashboard A set of graphs connected to a time window selected by the end user. The graphs show the number of people travelling using a particular transport mode. When a second time window is selected, the delta between the periods is also shown. Problem-setting, policy formulation, policy implementation and evaluation: Policy impact of extending the introduction of school streets in Mechelen. PoliVisu 6 6.2 Vlaanderen (Mechelen) Idem Idem Visualising Visualising the location of the school street sensor and sensors in the surrounding streets. Line data of road segments where a traffic sensor is installed: - Location (X, Y) - Categorisation (school street vs surrounding streets) - 2D Map Idem school street sensor locations using street segments (to anonymise the exact location of the sensor placed at a citizen's home). Idem PoliVisu 6 6.3 Vlaanderen (Mechelen) Idem Idem Visualising the use of transport modes, including the visualisation of policy targets (e.g., the number of counted cyclists at the school gate). - Time (date, hour) / Begin time, End time - Counts per transport mode - Sensor group (school street, surrounding streets) - Bar chart (advanced) Idem Combination of bar charts (with the possibility to switch categories on and off), allowing comparison between time periods, combined with the visualisation of a policy norm. Idem PoliVisu 6 6.3 Vlaanderen (Mechelen) Idem Idem Visualising traffic over the course of the day, a school street was implemented to assess the school street effect. - Time (date, hour) / Begin time, End time - Counts per transport mode - Sensor group (school street, surrounding streets) - Comparative trend analysis line chart Idem Trend analysis line chart visualising each transport mode to evaluate the effect of a school street implementation compared to the hours before and after the school street closure. Idem PoliVisu 6 6.4 Vlaanderen (Mechelen) Idem Idem Visualisation of the modal split (percentage of measured transport modes - walking, cycling, car, heavy vehicles). - Grouping (before/after implementation & school street/surrounding streets) - Modal split percentages - Pie chart (modal split) Idem Pie charts visualising the modal split (school street and neighbourhood) and between two periods in time (if a second period has been selected). Idem PoliVisu 7 7.1 Vlaanderen (SintNiklaas) As a city official, citizen, I want to quantify the effects of a new traffic scheme in terms of the amount, kind of traffic and speed on the affected roads. Policy objective: Implementation of a traffic scheme to decrease flowing traffic Policy action: Implementing a local traffic scheme Visualisation of the evolution in traffic volumes and driving speed in the affected streets. - Time (date, hour) - Counts per transport mode - Driving speed - Sensor location & road segment location - Traffic count data mobility dashboard - 2D Sensor location Map - Stacked bar and line charts (detailed traffic volumes) - Histogram (driving speed) - Pie chart (modal split) Telraam dashboard Dashboard combining 2D sensor location map, stacked bar & line charts depicting traffic volumes, histograms depicting driving speed and a pie chart depicting the modal split. Policy design, implementation support and evaluation: Impact of implementing a new traffic scheme in a neighbourhood in SintNiklaas. Compair 7 7.2 Vlaanderen (SintNiklaas) Idem Idem Geospatial overview of the sensor locations to get an overview of the area. - Sensor location & road segment location - Traffic volumes - 2D Sensor location Map Idem 2D map with sensor locations (road segments), segment colour depicting relative traffic volume. Idem Compair 7 7.3 Vlaanderen (SintNiklaas) Idem Idem Visualisation of the detailed traffic volumes of a street segment (hour of the day spanning a multiple-day period). - Time (date, hour) - Counts per transport mode - Sensor location & road segment location - Stacked bar and line charts (detailed traffic volumes) Idem Stacked bar chart (traffic modes), for each hour over a period of one week; Line chart depicting the traffic volumes per transport mode for each hour of one day. Idem Compair 1_VisualisationCases_AnalysisData Table_A2 Cases_VisualisationMethodologySTEPS 16 TABLE RESEARCH DATA 2: Visualisation Methodology - Steps Caption: Visualisation methodology step-by-step approach (case level) Case Nr Case title Visualisation content Policy Element (Step 1) Visualisation Goal (Step 2) Description of main data fields (Step 3) Visualisation Techniques (Step 4) Functional Specifications (with a focus on visualisation specs) (Step 5) Specify visualisation(s) and Tools (Step 6) Case visual nr. Initial request(sProcessed personal data involved Reasoning 18 Pilsen (CZ), Ghent (BE) - Digital Twin, Impact of road closures Visualising traffic volumes, air quality impact, and noise pollution impact (absolute volumes and deltas) in a 3D Digital Twin environment - case road closures on an existing network Problem-setting: Getting insight into the effect of a road closure measure Display on a 3D map the effect on traffic volume, air quality and noise pollution caused by traffic of changes in existing infrastructure Output of: - 3D Visualisation - Location of the road closure for car traffic (including driving directions) - Traffic model (traffic volumes including deltas) - Air quality (PM, NO2) (volumes including deltas) - Noise (DB) (volumes including deltas) Digital Twin visualisation depicting: - 3D map visualisations of buildings and infrastructure - Heatmap visualising differences (delta) for PM and NO2 (air quality), DB - Line segments (colour and thickness) for traffic volumes - 3D Buildings to depict street canyons - Visualise the city in 2D and 3D - Add traffic measures to the existing terrain (as input for the simulation) - Visualise the implemented traffic measures (e.g. road closure sign) - Visualisation of the road network volumes (model calculation results) - Visualisation of the road network volume changes (model calculation results) - Visualisation of the impact of changes in air quality - Visualisation of the impact of changes in air noise distribution (by traffic) DUET Cesium-based Local Digital Twin prototype - 2D/3D Traffic model volume delta map - Traffic Modeller (LDT integration) - 3D Air quality delta map - TNO, VITO - 3D Noise Distribution point map - TNO 18.1 - 18.3 Impact of road closures No The traffic data used is collected via traffic loops, which doesn't contain personal information 19 Pilsen (CZ) - Digital Twin, Ring road construction impact Visualising traffic volumes, air quality impact, noise pollution impact (absolute volumes and deltas) in a 3D Digital Twin environment - case new road infrastructure Problem-setting: Getting insight into the effect of a new ring road Display on a 3D map the effect on traffic volume, air quality and noise pollution caused by traffic of new road infrastructure impacting the city Output of: - 3D Visualisation - Location of the road closure for car traffic (including driving directions) - Traffic model (traffic volumes including deltas) - Air quality (PM, NO2) (volumes including deltas) - Noise (DB) (volumes including deltas) Digital Twin visualisation depicting: - 3D map visualisations of buildings and infrastructure - Heatmap visualising differences (delta) for PM and NO2 (air quality), DB - Line segments (colour and thickness) for traffic volumes - 3D Buildings to depict street canyons - Visualisation of the road network volumes (model calculation results) - Visualisation of the road network volume changes (model calculation results) - Visualisation of the impact of changes in air quality - Visualisation of the impact of changes in air noise distribution (by traffic) - Visualisation of the new ring road trajectory DUET Cesium-based Local Digital Twin prototype - Reusable 2D Traffic model volume map - Traffic Modeller (LDT integration) 19.1 Ring road construction impact No The traffic data used is collected via traffic loops, which doesn't contain personal information 20 Pilsen (CZ) - Digital Twin, Solar equipment locations in the city park Visualising the impact of 3D terrain assets (e.g. buildings, other constructions, e.g. bridges, trees) on the potential shadow impact on solar panels in a Digital Twin Policy action: Simulating the shadow impact on solar equipment efficiency Display in 3D the sunshine's impact on locations for every date and time Output of: - 3D Visualisation of buildings, trees (and other meaningful infrastructures) and incidence of light Digital Twin visualisation depicting: - 3D map visualisation of buildings and infrastructures - Shadow impact model visualisation - Navigatable 3D visualisation of city infrastructure - CRUD solar infrastructure location - Shadow impact simulation and visualisation of every moment during the year - Time navigation DUET Cesium-badsed Local Digital Twin prototype - 3D Solar impact map (Cesium) 20.1 Solar equipment impact visualisation No No private personal data are involved * PM = Particular Matter / BC = Black Carbon / CO² = Carbon Dioxide / DB = Noise 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 17 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 1 Dynamic Exposure Visualisation Dashboard (DEV-D) Visualisation of geospatial & time-based data measuring air quality and ambient conditions like humidity and temperature to interpret route conditions Dashboard visualising travel routes, fine dust and BC exposure in line graphs (normal and cumulated graphs) Air quality data (PM), ambient data (temperature, humidity), GPS coordinates Geospatial & time-based visualisation of IoT data (air quality and ambient conditions) Dynamic Exposure Visualisation Dashboard Berlin (DE), Flanders (BE) 1.1 COMPAIR Project Development 1 2D Trip visualisation map Spatial visualisation of individual trips, visualising the air quality (PM levels) along the route 2D Map route visualisation depicting road segments PMexposure levels Air quality data (PM), ambient data (temperature, humidity), GPS coordinates Geospatial & time-based visualisation of IoT data (air quality and ambient conditions) Dynamic Exposure Visualisation Dashboard Berlin (DE), Flanders (BE) 1.2 COMPAIR Project Development 1 Air Quality exposure line chart Line chart visualisation of exposure levels compared to the WHO air quality minimum target Line chart depicting distance or time with the fine dust particles (μg/m2) compared with the WHO guideline or black carbon (ng/m3) exposure Air quality data (PM, BC), ambient data (temperature, humidity), GPS coordinates Geospatial & time-based visualisation of IoT data (air quality and ambient conditions) Dynamic Exposure Visualisation Dashboard Berlin (DE), Flanders (BE) 1.3 COMPAIR Project Development 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 18 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 1 Air Quality Cumulative exposure line chart Line chart visualisation of cumulative exposure levels compared to the cumulative WHO air quality guideline Line chart depicting distance or time with the cumulative fine dust particles (μg/m2) compared with the cumulative WHO guideline or cumulative black carbon emission (ng/m3) exposure Air quality data (PM, BC), ambient data (temperature, humidity), GPS coordinates Geospatial & time-based visualisation of IoT data (air quality and ambient conditions) Dynamic Exposure Visualisation Dashboard Berlin (DE), Flanders (BE) 1.3 COMPAIR Project Development 1 Air Quality Cumulative exposure line chart Line chart visualisation of the inhaled dose exposure model output Line chart depicting distance or time with the cumulative fine dust particles (μg/m2) Air quality data (PM, BC), ambient data (temperature, humidity), GPS coordinates Geospatial & time-based visualisation of IoT data (air quality and ambient conditions) processed by an inhaled dose simulation algorithm Dynamic Exposure Visualisation Dashboard Berlin (DE), Flanders (BE) 1.4 COMPAIR Project Development 2 Floating car data mobility dashboard Overview of the historical traffic flows Dashboard, multiple visualsations Floating car data Multiple types Custom built dashboard with Angular Issy-LesMoulineaux (FR), Solva (BE) 2.1 PoliVisu Project Development 2 Traffic speed and delay time distribution histogram Overview of the speed and time delay (during the day) Line chart (single line) Floating car data Graph traffic time and speed delay processing (hourly) Custom built line graph with Angular Issy-LesMoulineaux (FR) 2.1 PoliVisu Project Development 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 19 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 2 Line-based free flow map Overview of the speed compared to the free float speed on a road segment Line map visualisation (line segment colour scale) Floating car data Geospatial & time-based visualisation of traffic redistribution free flow speed Custom built free flow map with Angular Issy-LesMoulineaux (FR) 2.1 PoliVisu Project Development 2 Table (lost time) Overview of the most congested days and most congested road segments Table Floating car data Traffic time and speed delay processing (hourly) Custom built matrix table with Angular Issy-LesMoulineaux (FR) 2.1 PoliVisu 2 Free-flow distribution heatmap matrix Hour/day overview of the free flow speed Matrix table (value, colour scale) Floating car data Graph Traffic time delay processing (hourly/daily) Custom built matrix table with Angular Issy-LesMoulineaux (FR) 2.1 PoliVisu Project Development 3 Average speed control dashboard Dashboard providing historical and recent data about average speed control zones Dashboard visualising average speed control data using bar charts, pie charts, line charts and matrix visualisations Pseudonymized historical ANPR data Geospatial & time-based processing of pseudonymized ANPR data Macq Sa - M3, custom built Policezone Voorkempen (BE) - PoliVisu Project Development 3 Heatmap Visualising the relative percentage of infractions at a typical moment during the week Heatmap (time, infraction percentage) Pseudonymized historical ANPR data Geospatial & time-based processing of pseudonymized ANPR data Macq Sa - M3, custom built Policezone Voorkempen (BE) 3.1 PoliVisu Project Development 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 20 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 3 Flow rate diagram Visualising the average speed relative to the number of vehicles and time period (during the day) Flow rate diagram (average speed, number of vehicles) Pseudonymized historical ANPR data Geospatial & time-based processing of pseudonymized ANPR data Macq Sa - M3, custom built Policezone Voorkempen (BE) 3.2 PoliVisu Project Development 3 Average Speed Control Infraction Histogram Visualising the relative percentage of infractions and near infractions over time to monitor longterm effects Average speed control historgram (infraction percentage, time) Pseudonymized historical ANPR data Geospatial & time-based processing of pseudonymized ANPR data Macq Sa - M3, custom built Policezone Voorkempen (BE) 3.3 PoliVisu Project Development 4 Sankey distribution diagram Distributions of origin destination between places (local communities & cities) Sankey diagram (line diagram - line thickness, line colour) Floating car data Graph processing of origin/destination of displacement counts TomTom Move Solva (BE) 4.1 PoliVisu Existing Toolset 4 OriginDestination matrix diagram and heatmap Origin/destinatio n distribution diagram, visualizing the distribution of trips between local communities and in the community itself Matrix table (value, colour scale) Floating car data Graph processing of origin/destination displacement counts TomTom Move Solva (BE) 4.2 PoliVisu Existing Toolset 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 21 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 4 Traffic flow line/trip distribution map Origin/destinatio n distribution to get geospatial insights into displacement patterns Line and area map visualising trips (line coulor - grey scale, area colour fade out) Floating car data Geospatial & time-based processing and visualisation of origin/destionatio n displacment counts TomTom Move Solva (BE) 4.3 PoliVisu Existing Toolset 5 Policy Monitoring Dashboard for Traffic and Air Quality Sensor Getting insights into the effects of measured traffic and air quality before and after a reference moment Dashboard, multiple visualsations Fixed traffic sensors (measuring multiple traffic modes), fixed air quality sensors (fine dust, black carbon, NO2) Geospatial & time-based visualisations Compair Policy Monitoring Dashboard (PMD) Vlaanderen (Herzele) 5.1 COMPAIR Project Development 5 2D Sensor Location Map Geospatial overview of fixed sensor location to get an overview of an area 2D Map with sensor locations (line segments, points depending on the sensor type) Fixed traffic sensors (measuring multiple traffic modes) (Herzele, Mechelen) Fixed air quality sensors (fine dust, black carbon, NO2) (Herzele) Map visualisation Compair Policy Monitoring Dashboard (PMD), Custom-built Dashboard for school streets Vlaanderen (Herzele, Mechelen, Sint-Niklaas) 5.1; 6.2; 7.2 PoliVisu; COMPAIR Project Development 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 22 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 5 Bar chart Overview of the traffic and air quality impact Bar charts visualise positive and negative values before and after a reference period Fixed traffic sensors (measuring multiple traffic modes), fixed air quality sensors (fine dust, black carbon, NO2) Bar chart Compair Policy Monitoring Dashboard (PMD) Vlaanderen (Herzele) 5.2 COMPAIR Project Development 6 school street implementation dashboard Dashboard providing historical and live data about the traffic in and around school streets to evalu! ate a school street implementation Dashboard visualising traffic count data around schools using bar charts, line chars, modal split pie charts and trend analysis line charts. Telraam traffic count data (aggregated data of traffic counts per transport mode), location data (schools) and weather information Geospatial & time-based processing and visualisation of traffic count data Custom built with Angular Mechelen (BE) 6.1 PoliVisu Project Development 6 Bar chart (advanced) Visualising bike use, including links with the policy targets, and the possibility to hide and show categories Bar chart (advanced) including other information (policy goal and weather conditions) Number of cyclists, policy target time-based processing and visualisaiton of traffic count data (cyclists) Custom built with Angular Mechelen (BE) 6.3 PoliVisu Project Development 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 23 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 6 Comparative trend analysis line chart Analysing the difference in use of transport modes between two time periods Line graph visualising the difference between two periods Number of road users time-based processing and visualisaiton of the difference in traffic count data between two periods (cyclists, pedstrians, cars, big vehicles) Custom built with Angular Mechelen (BE) 6.3 PoliVisu Project Development 6 Line chart (advanced) Visualising the traffic during the hours of the day a school street was implemented Line chart (advanced) including weather information and the possibility to switch on and off categories Number of road users, weather information time-based processing and visualisaiton of traffic count data (cyclists, pedstrians, cars, big vehicles) Custom built with Angular Mechelen (BE) 6.3 PoliVisu Project Development 6 Modal split pie chart Modal split visualisation (distribution between transport modes) Pie chart visualising the modal split Number of road users time-based processing and visualisaiton of traffic count data (cyclists, pedstrians, cars, big vehicles) Custom built with Angular Mechelen, Sint-Niklaas (BE) 6.4; 7.5 PoliVisu Project Development, Telraam.net dashboard 7 Traffic count data mobility dashboard Visualisation of the Telraam.net traffic sensor data counting pedestrians, cyclists, cars and big vehicles and vehicle speed Dashboard, multiple visualsations Telraam citizen science sensors time-based AI picture processing anonymised and aggregated Telraam.net dashboard Sint-Niklaas (BE) 7.1 COMPAIR Existing Toolset 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 24 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 7 Stacked bar and line charts (traffic volumes) Visualisation of detailed traffic volumes of a street segment (hour of the day for a multipleday period) Stacked bar and line chart Telraam citizen science sensors time-based AI picture processing anonymised and aggregated Telraam.net dashboard Sint-Niklaas (BE) 7.3 COMPAIR Existing Toolset 7 Histogram (driving speed) Visualisation of the traffic speed to get an overview of the traffic behaviour Histogram (driving speed, and V85 percentile over daytime) Telraam citizen science sensors time-based AI picture processing anonymised and aggregated Telraam.net dashboard Sint-Niklaas (BE) 7.4 COMPAIR Existing Toolset 8, 10 2D Interactive heatmap dashboard Better insights in the location and type of incidents Heatmap displaying the accident hotspot locations (colour and area size) combined with other data sources Anonymized accident data + other relevant data sources (for ex. schools, average speed control zones) Geospatial & time-based processing and visualisation of road accidents WebGLayer Flanders (BE), Pilsen (CZ) 8.1; 10.1 PoliVisu Project Development 8, 10 Interactive charts Better insights into the location and type of attribute data Interactive bar and line charts allowing interactive selection Geospatial & time-based dataset with attribute data Geospatial & time-based processing WebGLayer Flanders (BE), Pilsen (CZ) 8.1; 10.1; 11.1; 11.2 PoliVisu 9 Polygon choropleth map Map visualizing dorm student movements during the academic year Choropleth map (Colour scale), base road map, main university and high-school buildings Mobile phone data (location, time) of a selected group based on a geotime-based profile selection Geospatial & time-based processing and visualisation of mobile phone data QGis Ghent (BE) 9.1; 9.2 PoliVisu Existing Toolset 1_VisualisationCases_AnalysisData Table_A3 VisualisationTechniques 25 TABLE RESEARCH DATA 3: Visualisation techniques overview Caption: Overview of the used visualisation techniques, characteristics and use Pilot Visualisation technique Characteristics Use Case visual nr. Project Tool nr Name Visualisation goal (related to the visualisation technique) Visualisation type Type of input Type of processing Tools Pilots Nr Name (project dev / existing) 11 2D Interactive traffic line map Visualising the traffic level and intensity of each road segment 2D Interactive line map visualisation (using a colour scale), interactive histograms and calendar selector. Traffic sensors (traffic loops and counting cameras) Geospatial & time-based processing and visualisation of traffic conut data WebGLayer Pilsen (CZ) 11.1; 11.2 PoliVisu Project Development 12 2D Traffic volume delta map Visualising the traffic volume changes between two moments in time to evaluate a traffic increase or decrease Line segments (road segments) visualisation (colour scale) Traffic model (model based on traffic counts, census and road network data) Geospatial & time-based processing and visualisation of traffic model data Traffic modeller Pilsen (CZ) 12.1 PoliVisu Project Development 13, 19 2D Traffic model volume map Visualising the traffic volumes of a traffic model calculation to evaluate traffic volumes and volumes related to road capacity Line segments (road segments) visualisation (colour scale, line thickness) and absolute numbers Traffic model (model based on traffic counts, census and road network data) Geospatial & time-based processing and visualisation of traffic model data Traffic modeller Pilsen (CZ) 13.1; 19.1 PoliVisu Project Development 1_VisualisationCases_AnalysisData Table_2_Prep_Scoring 32 Athens (GR) Digital Twin - Green squares planning Policy Design Policy implementation Policy evaluation Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing 1 1 1 1 1 17 H2020 DUET Type: DIGITAL TWIN (DIGITAL TWIN VISUALISATION) Athens (GR) Digital Twin - Traffic load & creation of a pedestrian and cycling route Policy Design Policy implementation Policy evaluation Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing 1 1 1 1 1 18 H2020 DUET Type: DIGITAL TWIN (DIGITAL TWIN VISUALISATION) Pilsen (CZ), Ghent (BE) Digital Twin - Impact of road closures Policy Design Policy implementation Policy evaluation Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing 1 1 1 1 1 1 1 1 19 H2020 DUET Type: DIGITAL TWIN (DIGITAL TWIN VISUALISATION) Pilsen (CZ) Digital Twin - Ring road construction impact Policy Design Policy implementation Policy evaluation Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing 1 1 1 1 1 1 1 1 20 H2020 DUET Type: DIGITAL TWIN (DIGITAL TWIN VISUALISATION) Pilsen (CZ) Digital Twin - Solar equipment locations in the city park Policy Design Policy implementation Policy evaluation Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing 1 1 1 1 1 1 Totals Overview results Policy Design Policy implementation Policy evaluation Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing 18 10 7 9 10 4 7 15 14 7 1_VisualisationCases_AnalysisData Table_2_Prep_Scoring 33 Visualisation solution Policy Design Policy implementation Policy evaluation MATCH Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing Dashboard visualisation 6 3 1 2 2 1 5 5 5 3 -Visualisation DB 3 2 0 0 0 0 2 2 2 1 -PolicyDB 3 1 1 2 2 1 3 3 3 2 Intensity map visualisation 6 4 1 1 2 1 1 4 4 1 Algorithm visualisation 1 1 1 1 1 1 1 2 1 1 Digital Twin visualisation 5 2 4 5 5 1 0 4 4 2 Totals 44 36 21 Policy Design Policy implementation Policy evaluation TOTALS Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing Dashboard visualisation 7 7 7 7 7 7 7 7 7 7 -Visualisation DB 4 4 4 4 4 4 4 4 4 4 -PolicyDB 3 3 3 3 3 3 3 3 3 3 Intensity map visualisation 6 6 6 6 6 6 6 6 6 6 Algorithm visualisation 2 2 2 2 2 2 2 2 2 2 Digital Twin visualisation 5 5 5 5 5 5 5 5 5 5 Totals 80 80 40 Policy Design Policy implementation Policy evaluation PERCENTAGE Problem-setting Policy formulation Scenario analysis Decision Implementation plan Implementation On-going monitoring Communication Impact assessment Problem (re)structur -ing Dashboard visualisation 85.71% 42.86% 14.29% 28.57% 28.57% 14.29% 71.43% 71.43% 71.43% 42.86% -Visualisation DB 75.00% 50.00% 0.00% 0.00% 0.00% 0.00% 50.00% 50.00% 50.00% 25.00% -PolicyDB 100.00% 33.33% 33.33% 66.67% 66.67% 33.33% 100.00% 100.00% 100.00% 66.67% Intensity map visualisation 100.00% 66.67% 16.67% 16.67% 33.33% 16.67% 16.67% 66.67% 66.67% 16.67% Algorithm visualisation 50.00% 50.00% 50.00% 50.00% 50.00% 50.00% 50.00% 100.00% 50.00% 50.00% Digital Twin visualisation 100.00% 40.00% 80.00% 100.00% 100.00% 20.00% 0.00% 80.00% 80.00% 40.00%