TEAM 14: Analytical Map of Incidents Registered by the Municipal Police in Plzeň, Czechia

TEAM LEADER: Jiri Bouchal (InnoConnect)

TEAM MEMBERS: Jan Ježek (InnoConnect), Alvaro Silva (InnoConnect), Václav Kučera (SITMP)

PROJECT IDEA:

Problem we solve

There is a lot of big data available in the connected cities of today. Often, this data is stored for restrained purposes without any deeper analysis and visualisation.  Users thus do not benefit from the data and from its understanding that would allow them to act based on the information obtained from data. Cities usually do not know how to work with their data further to get the knowledge out of it that could support the decision making.

Solution

The proposed application will help the city of Plzeň (Czechia) to  identify trends and patterns in their security-related data provided by the Municipal Police, e.g. to identify areas with the highest risk of minor criminality, streets with most frequent parking, driving or speed violations, locations with pedestrian or cyclist offences, or neighbourhoods with alcohol- and drug-related offences. The web application will bring the data into a map and make it possible to analyze it for trends and patterns.

It will allow interactive analysis of large spatial data, using WebGLayer heatmap technology.

Thanks to the solution, users will benefit from visual insights obtained from the data. They can drill into the data, look at different combinations of attributes (such as specific hours or days of the week), and understand where the records and the riskiest areas are located on the map.

The city’s manager for criminality prevention can use the solution to discover locations where the city security police measures should target. Police commanders can use the app to identify most risky areas to which the police officers shall be sent to increase safety of citizens. The public can benefit from the higher awareness about the security-related issues in the city.

Technology

The product is a web-based map application coupled with analytical tools. It runs on WebGLayer (webglayer.org), a unique javascript open source library developed for rendering heatmaps with built-in dynamic data filtering.

Main Features:

  • Highly interactive
  • Instant reaction to user actions (response time below 100ms)
  • visualisation of up to 1.5 million data records

The library is based on WebGL and uses GPU (graphical processing unit)  for fast rendering and filtering of data. Using commodity hardware (an average PC) the library can visualise hundreds of thousand of features with several attributes through a heatmap, point symbol map.  The library can render the data on the map provided by third party libraries (e.g. Mapbox, OpenLayers, Leaflet, GoogleMap API).

Main advantages of our technology compared to common products on the market:

  • Interactive data filtering: Static images cannot provide sufficient representations of data, and a high level of interactivity is desired. Zooming and panning in geographic space is obvious, but interactive data filtering in various views that our solution provides is not a common feature nowadays.
  • Scalability: Efficient visualization is a key approach to understand large datasets. Scalability represents one of the key challenges from the perspective of visual encoding (the encoding must overcome visual clutter and over plotting) as well as interactivity performance. Our solution can efficiently visualise up to 1.5 data records while keeping low response times.
  • Interaction responsiveness (response time in milliseconds): Once interaction is enabled, the response time is essential. However, large-scale data requires advanced algorithms and approaches. Server side data processing may suffer from network latency. Our solution renders and filters the data on the client side using the GPU, no server side data processing occurs.
  • Modest hardware infrastructure demands: Traditional web mapping in geographical information systems (GIS) often demand infrastructure maintenance of spatial databases, and specific server side software such as MapServer or GeoServer.

Data

Incidents reported by the Municipal Police of Plzeň from January 1, 2015 to December 31, 2015.

No. of Data Records: 45216.

Data source: Municipal Police Plzeň http://www.mpplzen.cz/

The data for the first release of the application is provided as a sample DB export. It’s planned for the future that the data will be regularly updated and provided by the city through an API.

NOTE: even though the data was anonymised, it contains sensitive data that the owner of the data currently cannot make public. Therefore, due to security reasons, the dataset is currently not available as open data. The application will therefore be protected by a password and at this stage will not be available to public. However, it can be demonstrated during the hackathon presentations. It’s planned that after future prior agreement with the Municipal Police, a new release of the app might be developed with a subset of data that can be made available to public.

Support

The solution is developed within the PoliVisu project (polivisu.eu)

TEAM 13: Arctic Geodata and Fishery Statistics

TEAM LEADER: Torill Hamre (NERSC)

TEAM MEMBERS: Bente Lilja Bye (BLB), Arnfinn Morvik (IMR)

PROJECT IDEA:  The targeted area is sustainable aquaculture and bio-economy. We want to combine different types of met-ocean data (e.g. ice edge, SST) and fishery statistics to investigate potential links between climate change and activity in the polar region in and around Svalbard. We will evaluate if the FAIR principles are met for the chosen variables, using Copernicus, BarentsWatch and other open data resources. Accessibility and functionality of the related APIs will be assessed, and whether the chosen APIs can jointly provide new information.

APIs for candidate data sources:

IMR Zooplankton Norwegian Sea: http://metadata.nmdc.no/metadata-api/landingpage/71dd5275cb3bb9f57028dd7bcdc280a8

TEAM 12: Delimiting of Agro-Climatic Zones

TEAM LEADER: Karel Jedlička, Pavel Hájek

TEAM MEMBERS: Karl Gutbrodt, Marcela Doubkova, Apurva Kochar

PROJECT IDEA: The idea is to provide local Agro-climatic maps by processing detailed EO data and climate model data.

Current climate zones maps are very generic. These show large areas and display only some differences in topography. Characteristics such as seaside buffer zones, weather divides or South-North differences are usually not accounted. The idea is to provide local agro-climatic maps by processing detailed Earth Observation data for topography and land cover.

Such improvements in the climate zones would support local/within-field management strategies. For researchers it may be of interest to use this dataset for decisions related to field trial (climatic) representativeness. Agronomists and insurances may find this dataset useful for risk assessment.

Last but not least, researchers and advisors may find important to check the impact of climate change on given area and decide about future management strategies.

The local climate maps will take following factors into account:

  • General weather conditions (large-scale weather models)
  • Local topography (elevation,, with North/South slopes
  • Buffer effects, such as lakes, sea or swamps
  • Soil types.

Data sources:

  • Weather datasets: ERA5 (ECMWF), NEMS30 (meteoblue).
  • Topography maps: EU-DEM,
  • Land cover / soil maps (JRC)

TEAM 11: Expanding Open Land Use Map by Terrain Characteristics

TEAM LEADER: Karel Jedlička

TEAM MEMBERS: Marcela Doubková,  Dmitrij Kožuch

PROJECT IDEA: The idea is to expand Open Land Use map by computing main terrain characteristics of agricultural fields (LPIS blocks). So far for computation two datasets will be used 1arcsecond DEM dataset by USGS and Open Land Use map (for masking fields). The experimental area will be Weinviertel (province located in the norteast of Lower Austra).

So far it is possible to get the main terrain characteristics of the field by entering its unique id in Open Land Use dataset. For example here are those characteristics for the field with id 10145238 .

{‘min_elevation’: 186.64203, ‘max_elevation’: 196.92177, ‘mean_elevation’: 190.70232, ‘median_elevation’: 190.44339, ‘min_slope’: 0.7573741, ‘max_slope’: 1.5695069, ‘mean_slope’: 1.2346609, ‘median_slope’: 1.2555954, ‘min_azimuth’: -179.98296, ‘max_azimuth’: 179.62314, ‘mean_azimuth’: -13.073845, ‘median_azimuth’: -30.859669}

Otherwise as well as get the statistics it is also possible to download characteristics as TIF images:

INSPIRE Hackathon 2018 – Special Prize on Security

The INSPIRE Hackathon organisers will award a special prize in the value of 1000 EUR to a team that will address in the best possible way the issue of security, considering aspects related to the security of citizens and countries as the ones addressed in the framework of the NextGEOSS and EVER-EST projects. In the scope of this prize, security doesn’t include security of data and information from the ICT point of view.

More information about the hackathon can be found here.

TEAM 10: Location Intelligence from Multi-Variate Spatial Analysis

TEAM LEADERS: Runar Bergheim, Karel Charvat

TEAM MEMBERS: Petr Uhlir, Raitis Berzins, Dmitrij Kozuk, Milan Kalas

PROJECT IDEA:

A lot of energy has gone into the development of precision data both with regards to fundamental geospatial data such as basemaps and thematic data serving a single purposes for specific and narrow target audiences.  This idea seeks to use such data to elaborate detailed characteristics about places based on the co-occurrence of certain features or phenomena.

 

An example of how such characteristics could be used, let us consider the following. The accessibility of a place may be described in terms of its proximity to transport hubs for air, train and road transport. That gives a snapshot of the current state of the area; however — by incorporating planned and future developments, it is possible to characterize a place by how it is likely to be two years from now. The climate of a place can be described in terms of monthly averages, averaged over 50 years of aggregated data for precipitation, air temperature, sea temperature, cloud cover, snow cover etc. The terrain can be characterized in terms of its ruggedness, whether it is a platou, a plane, coastal, mountaineous or otherwise. There is a near infinite number of characteristics that can be considered — in themselves they are not necessarily particularly useful — but combined the right way they may predict trends and offer location insights that are useful both to individuals, private enterprises and regional development bodies.

 

I.e. by identifying all places in the mountains that has rugged terrain and that has a long and steady period of snow cover with cold frequent sunny days — and with a new infrastructure hubs being developed within 75 minutes drive away — but scores low on availability of visitor oriented services we have established a dormant economic potential. This sort of location intelligence is thus far the material of reports.

 

This team will be operating in a mixed technical and non-technical manner. On the one hand we will expand upon the business cases in an exploratory manner through conversation; on the other we will try to identify practical sources and algorithms to determine key characteristics for places, taking as a starting point a seed database of about 20 000 locations and a bunch of climate, land-cover and landscape characteristics that have already been calculated.

TEAM 9: SeWa – Sentinel Watcher

TEAM LEADER: Marek Šplíchal (Lesprojekt)

TEAM MEMBERS: Jan Sháněl (MUNI)

PROJECT IDEA: Map based web application for identifying of usable remote sensing data from Sentinel satellites. A user can choose one or multiple positions (for example fields, forests etc.) and the application prepare a forecast based on location, minimal satellite elevation and minimal crossing duration for Sentinel 2A and 2B satellites. The application calculates timetable of satellites crossing time and weather (clouds) forecast as a result. User gets information when his selected position(s) can be photographed and which imagery can be used for further processing.

The DataBio Project Starts Trials of 26 Bioeconomy Pilots

7th August 2018, DataBio Press Release:

The development of a sustainable bioeconomy in Europe is expected to get a big boost from Big Data technologies thanks to a project co-funded by the EU’s Horizon 2020 programme.

The European Union’s Data-Driven Bioeconomy (DataBio) project has announced the launch of 26 different pilot trials, which are already underway in 17 countries. They will provide “real world” insights relevant for policymakers and producers – the farmers, foresters and fishermen engaged in Europe’s bioeconomy.

DataBio is using Big Data technologies to support the growth of Europe’s bioeconomy. More specifically, the project is handling massive flows of data collected through sensors placed in the soil and air, as well as from aerial and satellite imagery.

The DataBio consortium includes 48 partners from 17 countries and over 100 associated organisations.

The project’s mission is driven by the development, use and evaluation of the 26 new pilots covering agriculture (13), forestry (7) and fishery (6). The aim is to contribute to the production of the best possible raw materials from the three sectors to improve the output of food, energy and biomaterials.

The project is deploying over 90 state-of-the-art Big Data, Earth Observation and ICT technologies, linked together through the DataBio Platform. DataBio modelled the pilots and the technologies from a number of perspectives (e.g. technical and data, business motivation and processes, strategic) and developed the first version of the DataBio platform. The technologies have been matched and combined with each other to form innovative complex solutions for each pilot.

Now that the pilots have started their first trials, the project is one step closer to achieving its goal to demonstrate how these solutions can offer real added value to bioeconomy businesses.

The DataBio project coordinator, from INTRASOFT International, Dr Athanasios Poulakidas, said: “We are very excited, anticipating tangible success stories showing there is real value for all in using Big Data technologies in bioeconomy.”

Agriculture pilots

Precision agriculture in olives, fruits, grapes (Greece): A smart farming pilot to promote sustainable practices by providing policy advice on irrigation, fertilisation and pest/disease management. The exploitation of heterogeneous data, facts and scientific knowledge is aimed to facilitate decision-making and ensure smooth implementation of policy advice in the field. Deployed at three different sites in Greece, the pilots target olives, peaches and grapes.

Precision agriculture in vegetable seed crops (Italy): Harvesting plants at the right stage of maturity is vital to ensure the seed produced is of high quality. Currently, it is up to farmers, with the help of seed experts, to decide about harvesting and this is usually based on experience and observation. The scope of the pilot is to support farmers with the use of satellite telemetry.

Precision agriculture in vegetables seed crops (Netherlands): Potato growers aim to furnish them with higher and more predictable yields in a sustainable manner. Farmers will use a crop monitoring and benchmarking system using satellite data that provides information on the crop status based on weather data and greenness index data.

Big Data management in greenhouse eco-systems (Italy): This pilot implements genomic selection models, with particular focus on tomatoes, to support the greenhouse horticulture value chain.

Cereals, biomass and fibre crops (Spain): Using Earth Observation imageries and Internet of Things (IoT) sensor data, the pilot will map different areas in Spain and set up an informative management system for irrigation and early warning of heterogeneities or malfunctions of irrigation systems. The users of this service will be farmers, irrigation communities and public administrations.

Cereals, biomass and fibre crops (Greece): A smart farming pilot to promote sustainable practices by providing policy advice on irrigation. The exploitation of heterogeneous data, facts and scientific knowledge is aimed to facilitate decision-making and ensure smooth implementation of policy advice in the field. The target crop type is cotton.

Cereals, biomass and fibre crops (Italy): The pilot uses remote and proximal sensors for biomass crop prediction and management. The biomass crops include sorghum, fiber hemp and cardoon that can be used for several purposes including biofuel, fiber, and biochemicals respectively.

Cereals, biomass and fibre crops (Czech Republic):  To develop the web-based webGIS platform for mapping crop vigour, this pilot integrates Earth Observation data as a support tool for variable rate application of fertilisers and crop protection. This includes identification of crop status, mapping of spatial variability and delineation of management zones.

Machinery management (Czech Republic): This pilot is focused mainly on collecting telematic data from tractors and other farm machinery to analyse and compare to other farm data. The main goal is to collect and integrate data and receive comparable results. A challenge associated with this pilot is that a farm may have tractors and other machinery from manufacturers that use different telematic solutions and data ownership/sharing policies.

Insurance (Greece): To promote a damage assessment methodology and services dedicated to the agricultural insurance market, this pilot will eliminate the need for on-the-spot checks and to speed up the claims pay-out process. It uses data from Earth Observation platforms and Internet of Things agro-climate sensors to assess the impact of climate-related systemic perils (e.g. high/low temperatures, flood, drought) on high-value crops.

Farm Weather Insurance Assessment (Italy): The aim of this pilot is to provide and assess a test area of services for the agriculture insurance market, in particular risk assessment related to weather conditions and damage assessment. It is based on the analysis of satellite data, which is correlated with meteorological data and other ground-available data.

Common Agricultural Policy (CAP) Support (Italy and Romania): The objective of this pilot is to support the CAP by utilising Earth Observation data to identify the crop types in farm areas. Products and services will be fine-tuned to achieve requirements set out in the 2015-2020 EU CAP policy. The pilot will provide information layers and indicators to support European Paying Agencies with different levels of aggregation and details up to farm level.

Common Agricultural Policy (CAP) Support (Greece): This pilot evaluates a set of Earth Observation-based crop classification services, which deal effectively with the newly introduced CAP demands for systematic multi-crop agricultural monitoring, tracking and assessment of eligibility conditions. The proposed services use “traffic lights” colour-coding to protect the farmers against errors during the submission of greening applications.

Forestry pilots

Easy data sharing and networking (Finland): The Wuudis platform is used to interface with Finland’s forest authority. New networking features have been added, such as a work quality monitoring app. This will streamline and ensure transparency of subsidy payments, saving enormous amounts of time.

Monitoring and control tools for forest owners (Finland): This pilot adds crowdsourcing services to the Wuudis platform, interfacing with Finland’s forest service portal. This aims to better monitor forest damage (such as storms, snow, pests, diseases).

Forest damage remote sensing (Finland): This pilot aims to create a forest inventory monitoring service by using data collected by Unmanned Aerial Vehicles (UAVs) on Wuudis.

Monitoring of forest health (Spain): Using satellite data, Unmanned Aerial Vehicles (UAV) imagery and field data, this pilot is developing a methodology for the early detection and monitoring of plagues and diseases affecting forests. The study cases are Gonipterus affecting Eucalyptus and Phytophtora affecting Quercys Illex. The final product will be used by public administrations for optimal decision-making.

Invasive alien species control and monitoring (Spain): Using Big Data sources such as trading and travelling datasets, in addition to weather and climate information, this pilot is developing a methodology for the creation of Alien Invasive Species risk maps. The indexes obtained are related to the susceptibility of ecosystems to be invaded. The final product will be used by public administrations.

Web-mapping service for government decision-making (Czech Republic): Big satellite data are processed in novel ways to yield forest health trends. To do so, all available satellite observations are used, validated against extensive in-situ database of forest health. Resulting forest health maps are published as a web-mapping service and used by the Ministry of Agriculture for subsidy payments.

Shared multiuser forest data environment (Finland): Finland’s forestry service portal will be extended to offer more forest data services, including using crowdsourced ones and connectivity with the national service architecture. It will encourage the utilisation of standardised and open forest data for developing new services that will benefit the entire forestry sector.

Fishery pilots

Oceanic tuna fisheries immediate operational choices: The goal of this pilot is to improve vessel energy efficiency in oceanic tuna fisheries and engine preventive maintenance by providing support tools for operational choices, such as vessel loading, weather routing and condition-based maintenance, for Basque fishery vessels. Big Data approaches will build models using engine, propulsion, meteorological and historical data and the vessels design.

Small pelagic fisheries immediate operational choices: This pilot focuses on energy optimisation in Norwegian pelagic fisheries through operational support tools for deciding propulsion mode (diesel-electric, diesel-mechanic and various hybrid configurations) and power generation (use of shaft generator and auxiliary engines). The expected outcome is an on-board application that displays in real-time, current fuel consumption and the best alternatives for propulsion modes and energy production.

Oceanic tuna fisheries planning: The aim of this pilot is to use data from Earth Observation satellites and from buoys used by fishermen, to produce real-time fisheries route planning. This will also allow for eco-friendlier fisheries by means of consuming less fuel for fishing in a sustainable manner.

Small pelagic fisheries planning: This pilot aims to demonstrate how the use of Big Data technologies can provide crew and shipowners with information in a manner that benefits fisheries planning, for example for finding the best suitable fishing grounds. This pilot targets Norway’s small pelagic fisheries.

Pelagic fish stock assessments: The pilot aims to show that a combination of information from various data assets may help produce better fish population estimates for pelagic fish stocks in the Norwegian fishing zones. It is anticipated that a crowdsourced data collection effort combined with public/private data assets and data analytics can increase both the accuracy and precision of stock assessments.

Small pelagic market predictions and traceability: This pilot will leverage Big Data technologies for pelagic price prediction based on various market data relevant for pelagic fisheries, such as catch and landing data and International fisheries export and market data. Near-future pelagic price predictions will be used for catch value optimisation.

 

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