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Disruptive technologies accelerating data-driven policymaking in the public sector. On 20 September 2022, Policy Cloud, Decido, AI4PublicPolicy, DUET and Intelcomp pan-European projects and initiatives dedicated to using cloud for data-driven policy, have joined forces in the Data Driven Policy Cluster. Together they explore major challenges, trends and opportunities to improve public sector decision making that will deliver healthier, happier places to live and work. During the EGI 2022 workshop the cluster demonstrated technologies developed to advance decision making in the public sector. This report is the result of that online conference. &nbsp

The contribution describes Traffic Modeller (www.trafficmodeller.com) - a web map application for monitoring, analysis and prediction of traffic in a metropolitan area. The technical solution of Traffic Modeller is provided. This includes the mandatory and optional input data sources (such as road network, socioeconomic data describing behavior related to transport, real traffic measurements), the algorithms processing the measurements and traffic flow modelling algorithms. As an output of Traffic Modeller, various interactive maps portraying historical, current and predicted traffic data can be obtained. The paper includes the development and deployment of Traffic Modeller application for the smart city of Pilsen. Traffic Modeller outputs can play a crucial role in city policy-making. The resulting maps allow city planners to study the traffic behaviour while modelling various road network parameters (e.g. road closures, road capacity) and explore the effects of the changes in traffic flows in near real-time. The second part of the contribution describes typical use cases which can help policymakers in making smart, data-based and informed decisions

Třetí dimenze v GIS se dnes objevuje všude. Když někdo vidí perspektivu (nebo dokonce celou 3D vizualizace) 3D geografických dat, jeho první reakce je obvykle velmi pozitivní. Avšak po druhém pohledu nebo při delší práci s 3D GIS si uživatel někdy neuvědomuje žádnou nebo minimální přidanou hodnotu, doprovázenou nevyžádanou složitost způsobená potřebou zacházet s 3D daty na 2D obrazovce (nebo obecně na nějakém 3D zobrazovacím zařízení). Příspěvek si klade za cíl představit výstupy z nasazeného online dotazníku, který se zabýval srovnáním řešení úkoly založené na 2D nebo 3D vizualizaci dat. Na základě těchto výstupů uvádíme několik příkladů osvědčených postupů skládající se z workshopů a cvičení, jak pracovat s 3D geografickými daty, následované příklady výstupů projektů práce s 3D geografickými daty. Poté jsou diskutovány výhody a nevýhody takových případů použití. Zaměřili jsme se na případy, kdy použití třetí souřadnice má přidanou hodnotu ve srovnání s dvourozměrnými geografickými daty, jejich analýzou a vizualizace. visualization) of 3D geographical data, his or her first reaction is usually very positive. However, after a second look or a longer work with the 3D GIS, the user sometimes realizes no or minimal added value, accompanied by unsolicited complexity caused by the need to handle 3D data on a 2D screen (or on some 3D portraying device in general). The contribution aims to present outputs from a deployed online questionnaire, which dealt with comparison of solving tasks based 2D or 3D visualization of data. Based on such outputs we provide several best practices examples consisting of workshops and tutorials, how to work with 3D geographic data, followed by examples of projects’ outputs dealing with 3D geographical data. The pros and cons of such use cases are then discussed. We focused on cases where the use of the third coordinate adds value comparing to two-dimensional geographic data, their analysis, and visualization.

Big data technology (BDT) is a new technological paradigm that is driving the entire economy, including low-tech industries such as agriculture where it is implemented under the banner of precision farming (PF). Big data analytics system will then provide pilot managers with highly localized descriptive (better and more advanced way of looking at an operation), prescriptive (timely recommendations for operation improvement i.e., seed, fertilizer and other agricultural inputs application rates, soil analysis, and localized weather and disease/pest reports, based on real-time and historical data) and predictive plans (use current and historical data sets to forecast future localized events and returns). Presentation will be focused on two completely new domains of Big Data Visualisation and Analysis for Agriculture.

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Due to the changes in the climate, more extreme weather conditions occur. Weather extremes (such as heat, drought, freeze, etc.) are limiting conditions for the cultivation of agricultural crops. Long term monitoring of particular quantities such as temperature, humidity, wind conditions and so on are essential for decision making in agriculture. Since the monitoring is an expensive process, the physical monitoring stations are usually very coarse. The common approach to combine these data with a global model. This paper presents steps which can be done further, using temperature as an example. The paper first presents a workflow, how to create a denser model of temperature distribution from a course one model of temperatures. Next, the paper outlines a method of how to analyze the temperature spatial distribution in time. The aim is to use historical meteorological series (e.g. of temperatures) to help farmers and producers of agriculture’s products with decision making.