Open Data for Open Cites (OD4OC): Reuse of Open Data through Spatial Analysis

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (1 December 2018) | Viewed by 24826

Special Issue Editors


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Guest Editor
Nova Information Management School, Universidade Nova de Lisbon, Lisbon, Portugal
Interests: geographical information systems and science; sustainable smart cities; disaster risk management; open cities; open data

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Guest Editor
Geospatial Technology Research Group, Universitat Jaume I, Castellon, Spain
Interests: geographical information systems and science; spatial analysis; open data, GIS development of native/web apps; smart cities and open cities

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Guest Editor
Nova Information Management School, Universidade Nova de Lisbon, Lisbon, Portugal
Interests: geographical information systems and science; (spatial) decision support systems; data integration; spatial analysis; geographic user generated content; information infrastructures; GIS education and distance learning (e-learning); geographic information for development

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Guest Editor
Geospatial Technology Research Group, Universitat Jaume I, Castellon, Spain
Interests: smart cities/campuses; internet technologies; augumented reality; 3D models; Web 2.0; contextual systems; sensor networks and GIS application development

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Guest Editor
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Interests: ecosystem services; geographical information systems; remote sensing; spatial modelling; land use and land cover change; urban planning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Geospatial Technology Research Group, Universitat Jaume I, Castellon, Spain
Interests: distributed geographical information systems; smart cities; hybrid FOSS/proprietory interoperability

Special Issue Information

Dear Colleagues,

We invite contributions that cover all aspect of the use of Open Data and geospatial analysis in fostering inclusive, resilient, open and sustainable communities. 

The explosive growth of cities and the rapid expansion of broadband and data are intersecting at a time when the world faces serious challenges to achieving more sustainable development. Sensors and digital devices generate huge amount of data from which cities and governments can create indicators and learn new knowledge. Cities now have an important role to play in national and local open data initiatives. Data user communities are trying to tackle various urban challenges for many intermediaries that see open data as the key factor to improve their services. However, are cities ready to move forward in terms of open data? Are the current data user requirements taken into consideration in open data strategy? How are open data being used to make cities inclusive, safe, resilient and sustainable?

Hence, we invite papers describing original work in any of the following areas, but not limited to:

  1. Urban Analytics
  2. Open data-driven initiatives for effective community collaboration
  3. The use of Open Data in Smart sustainable cities
  4. Frameworks for sustainable community engagement
  5. Reuse of open geographic data
  6. Impact of Open Data in cities
  7. Impact of Open Geographic Data

This Special Issue will contain extended versions of selected papers presented at AGILE 2018 pre-conference workshop titled Open Data for Open Cities (http://opendata4opencities.uji.es) held in Lund, Sweden, 12 June, 2018. AGILE 2018 is the annual international conference on Geographic Information Science.

Dr. Adeoluwa Akande
Dr. Fernando Benitez
Prof. Marco Painho
Prof. Joaquín Huerta
Prof. Pedro Cabral
Prof. Michael Gould
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Open Data
  • Open Cities
  • Spatial Analysis
  • Smart cities
  • Reusability
  • Open Government
  • Impact of open data

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Published Papers (4 papers)

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Research

16 pages, 3288 KiB  
Article
Assessing Lisbon Trees’ Carbon Storage Quantity, Density, and Value Using Open Data and Allometric Equations
by Miguel de Castro Neto and Pedro Sarmento
Information 2019, 10(4), 133; https://doi.org/10.3390/info10040133 - 10 Apr 2019
Cited by 9 | Viewed by 5214
Abstract
Urban population has grown exponentially in recent years, leading to an increase of CO2 emissions and consequently contributing on a large scale to climate change. Urban trees are fundamental to mitigating CO2 emissions as they incorporate carbon in their biomass. It [...] Read more.
Urban population has grown exponentially in recent years, leading to an increase of CO2 emissions and consequently contributing on a large scale to climate change. Urban trees are fundamental to mitigating CO2 emissions as they incorporate carbon in their biomass. It becomes necessary to understand and measure urban tree carbon storage. In this paper is studied the potential of open data to measure the quantity, density, and value of carbon stored by the seven most represented urban trees in the city of Lisbon. To compute carbon storage, the seven most represented urban tree species were selected from an open database acquired from an open data portal of the city of Lisbon. Through allometric equations, it was possible to compute the trees’ biomass and calculate carbon storage quantity, density, and value. The results showed that the tree species Celtis australis is the species that contributes more to carbon storage. Central parishes of the city of Lisbon present higher-density values of carbon storage when compared with the border parishes despite the first ones presenting low-to-medium values of carbon storage quantity and value. Trees located in streets, present higher values of carbon storage, when compared with trees located in schools and green areas. Finally, the potential usage of this information to build a decision-support dashboard for planning green infrastructures was demonstrated. Full article
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14 pages, 10360 KiB  
Article
Quantifying Bicycle Network Connectivity in Lisbon Using Open Data
by Lorena Abad and Lucas Van der Meer
Information 2018, 9(11), 287; https://doi.org/10.3390/info9110287 - 17 Nov 2018
Cited by 8 | Viewed by 5879
Abstract
Stimulating non-motorized transport has been a key point on sustainable mobility agendas for cities around the world. Lisbon is no exception, as it invests in the implementation of new bike infrastructure. Quantifying the connectivity of such a bicycle network can help evaluate its [...] Read more.
Stimulating non-motorized transport has been a key point on sustainable mobility agendas for cities around the world. Lisbon is no exception, as it invests in the implementation of new bike infrastructure. Quantifying the connectivity of such a bicycle network can help evaluate its current state and highlight specific challenges that should be addressed. Therefore, the aim of this study is to develop an exploratory score that allows a quantification of the bicycle network connectivity in Lisbon based on open data. For each part of the city, a score was computed based on how many common destinations (e.g., schools, universities, supermarkets, hospitals) were located within an acceptable biking distance when using only bicycle lanes and roads with low traffic stress for cyclists. Taking a weighted average of these scores resulted in an overall score for the city of Lisbon of only 8.6 out of 100 points. This shows, at a glance, that the city still has a long way to go before achieving their objectives regarding bicycle use in the city. Full article
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26 pages, 1077 KiB  
Article
Motivation Perspectives on Opening up Municipality Data: Does Municipality Size Matter?
by Anneke Zuiderwijk, Cécile Volten, Maarten Kroesen and Mark Gill
Information 2018, 9(11), 267; https://doi.org/10.3390/info9110267 - 25 Oct 2018
Cited by 12 | Viewed by 4783
Abstract
National governments often expect municipalities to develop toward open cities and be equally motivated to open up municipal data, yet municipalities have different characteristics influencing their motivations. This paper aims to reveal how municipality size influences municipalities’ motivation perspectives on opening up municipality [...] Read more.
National governments often expect municipalities to develop toward open cities and be equally motivated to open up municipal data, yet municipalities have different characteristics influencing their motivations. This paper aims to reveal how municipality size influences municipalities’ motivation perspectives on opening up municipality data. To this end, Q-methodology is used, which is a method that is suited to objectify people’s frames of mind on a particular topic. By applying this method to 37 municipalities in the Netherlands, we elicited the motivation perspectives of three main groups of municipalities: (1) advocating municipalities, (2) careful municipalities, and (3) conservative municipalities. We found that advocating municipalities are mainly large-sized municipalities (>65,000 inhabitants) and a few small-sized municipalities (<35,000 inhabitants). Careful municipalities concern municipalities of all sizes (small, medium, and large). The conservative municipality perspective is more common among smaller-sized municipalities. Our findings do not support the statement “the smaller the municipality, the less motivated it is to open up its data”. However, the type and amount of municipality resources do influence motivations to share data or not. We provide recommendations for how open data policy makers on the national level need to support the three groups of municipalities and municipalities of different sizes in different ways to stimulate the provision of municipal data to the public as much as possible. Moreover, if national governments can identify which municipalities adhere to which motivation perspective, they can then develop more targeted open data policies that meet the requirements of the municipalities that adhere to each perspective. This should result in more open data value creation. Full article
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13 pages, 1747 KiB  
Article
What Smart Campuses Can Teach Us about Smart Cities: User Experiences and Open Data
by Roza Vasileva, Lucelia Rodrigues, Nancy Hughes, Chris Greenhalgh, Murray Goulden and Jeni Tennison
Information 2018, 9(10), 251; https://doi.org/10.3390/info9100251 - 12 Oct 2018
Cited by 39 | Viewed by 8004
Abstract
Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How [...] Read more.
Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scaled up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom, employing stakeholder interviews and user surveys, which aimed to gain insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges, such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, then open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities. Full article
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