key: cord-0949624-9yw7uphi authors: Massaro, Alessandro; Rossetti, Silvia title: Case study on city-airports: Datasets and calculation models date: 2021-01-30 journal: Data Brief DOI: 10.1016/j.dib.2021.106789 sha: d53c25feddfaddb88c29538325f6f1ac61983161 doc_id: 949624 cord_uid: 9yw7uphi Data have been collected over time, belonging the 2018th and 2019th, from airports owners, from stakeholders, from universities, from the net, and performing under GIS evaluation processes. Most of the collected data are geographic, economic, and financial statements of the different ownerships, maps about the airport and urban planning, and data about carriers and routes. Specifically, the GIS has been useful to the Network Analysis evaluations. The analysis results can be collected and used in the most comprehensive analysis of similar systems. The results summarize data about four different couples of small remote airports in the EU and their specific network systems [1], [2], [3], [4]. Therefore, the ongoing analysis wait to be extended to other similar systems. Management, Monitoring, Policy, and Law Specific subject area Notably, it analyzes airport management, planning systems, monitoring of resources' capability, policy on the various stakeholders, and related laws. Type of data Table Image Graph Figure Acquiring Data Methodology The data comes from searching on the net, using a notebook, or through a pen drive transferring, either using Windows Software or Google Applications. Data format Raw Parameters for data collection The data collection follows the scheme described in the Experimental Design, Materials, and Methods paragraph, using GIS software to evaluate the data. The set follows a complete acquisition of data on the matters of the ongoing search. The data collection follows the scheme described in the Experimental Design, Materials, and Methods paragraph, using GIS software to evaluate the data. The data acquisition follows effective utilization. Moreover, they come from relationships with some of the different stakeholders and universities. • These data help discover information and suggestions in different remote parts of the EU, evaluating intersections between airports and city planning systems. • The data could be useful to Researchers, Students, Professors, Managers, and Shareholders. • The data lead to an overall evaluation of connections between investments and planning, airports, and cities, improving connectivity, accessibility, and proximity in the transport systems. Data collected regards four couples of different airports, their cities, and the relative network system [5] [6] [7] [8] . The dataset comes from many internet portals for each analyzed region. Moreover, for two of the four couples, data are obtained directly from the respective owners, belong to various meetings in the ongoing search with Parma University. The GIS has drawn all the maps, all the vectorial files with CAD software, all the tables with Microsoft Excel, and all the Figures with Open Source Applications. For first, managing the multiple datasets by Excel, codified, and linked to the next use with the GIS software. The construction of the different maps (some in the article) regarding the four couples results by scaling various network systems with proper relationships. Directly from the earned analysis of the networks (the following data, kept out by the article, show the work of Norway's couple), it has been possible to summarize the diverse airport-cities results in a final SWOT Analysis. The following procedure describes the utilized methodology to build the different maps, substantially addicted to using the GIS software [9] [10] [11] . The data have been collected, transformed, and linked using various codes strictly related to each other following the analysis scheme from multiple sources. Fields Descriptors: The algorithm uses a priority queue on which performing three operations: the construction of the string, the extraction of the minimum element, and the reduction of an element's value. The computational complexity of Dijkstra's algorithm managing time can be a function of | V | e |E| (number of vertices and arcs). The data structure used for implementing the priority queue determines the complexity of the three operations and, consequently, that of the algorithm. In general, complexity, T Where the worst case is: The evaluation of airport connectivity has been managed to cross the data before and after NTP investments, changing the path's maximum speed. 1. The catchment area measurement has been done by Network Analysis tools, building two different types of maps: • Airports catchment areas by time of journey before and after investments; • Hospital catchment areas by time of journey before and after investments; The evaluation of airport catchment areas has been managed to cross the data before and after NTP investments, changing the path's maximum speed. • It has been possible to evaluate served, overserved, and unserved zones, by variation in time ( % in the years) of the economic features into the county, municipality, airport, and port sheets: • Financial critical figures for municipalities; • Investment, revenues, and expenditures in transports for counties; • Investment, revenues, and expenditures in tourism facilities; • Investment, revenues, and expenditures in health; • Investment, revenues, and expenditures in touristic real estate; The location-allocation analysis has been helpful to evaluate the different weights of the airports in the municipality demand. x ad [ x ( in jm )] 1 if aircraft a travels directly from node i to node j on day d, visiting node i for the n th time and node j for the m th time, 0 otherwise ( Fig. 1 ) y a 1 if aircraft a is used during the time horizon, 0 otherwise z ad [ z( in jm ) ] 1 if aircraft a travels from node i to node j on day d, visiting node i for the n th time and node j for the m th time, 0 otherwise ( z variables represent a connection on a specific trip without looking at the actual routing, so it could be a direct link, or it could be a connection with multiple legs. ) • Route system evaluation has been useful also to find the sheet of the best path to Gardermoen. • The weight of the existing route system has been managed by the cost of journeys, type of company, number of passengers (adding % variation of the passenger in the years) and tonne: -Some different kernel density maps are useful to evaluate the impact of actual routes: • Routes of Northern Region; • Routes of Halogaland; • Routes on petroleum basis. Routledge Advances in Regional Economics Aeroporti e territorio The Role of Secondary Airports For Today's Low-Cost Carrier Business model: the European Case Airline Operations and Management -A management Textbook Managing airports: An international Perspective Small regional airports operation: unnecessary burdens or key to regional development Airport Planning & Management Fundamental Research in Geographical and Information Analysis Salento Airport: remote for Italy, to gateway for the Mediterranean Region Air Traffic Development in Remote Regions: the Case of Brindisi Airport -Air Transport and Regional development Air Transport Implications in Tourist Destinations The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article. Supplementary material associated with this article can be found in the online version at doi: 10.1016/j.dib.2021.106789 .