Abstract
A 60-year-old Brazilian university campus reflects its development actions throughout history. The degradation of its water streams and automobiles prioritization has significantly impacted the ecosystems’ resilience and the university’s activities. This article explores data analytics and visualization of Wi-Fi authentication processes, whose data have been stored in the last 10 years. This noteworthy connections database is a powerful tool, still overlooked due to the remarkable risks for users’ privacy. Brazil has followed 2018 European regulations to protect data privacy, when working with personal data. Therefore, we present an anonymization process that prevents one to identify and distinguish a subject within a set of subjects of the database. Three studies illustrate our examination of data potential to understand the university’s dynamics. These inquiries present relevant contributions to the process of planning and implementing campus green areas at the rivers’ edges, pedestrian and cycle paths, and places to facilitate interdisciplinary encounters.
Keywords
Introduction
This article aims to demonstrate the effectiveness of the university’s Wi-Fi stored data in supporting campus resilience enterprises associated with university innovation. These data are generated by processes of device authentication in the access points distributed across the campus. Each log file associates user identification, location, and time and represents a powerful resource, but carries remarkable risks for users’ privacy. The great challenge is how to measure, communicate, and make feasible this significant resource potential. Through using data generated by Wi-Fi, this research project has recognized the campus rivers’ potential in providing enjoyable spaces and a network of new connections and multidisciplinary encounters throughout the campus.
If exploring data analytics and visualization supports an understanding of the academic community dynamics, they need a careful anonymization process. The debate around data misuse by companies and governments has led a revision of regulations concerning data privacy. Several countries substituted their data protection laws to more strict ones, due to data leakage events. After that, anonymization is the only possibility to work with personal data and not face strict privacy laws. With anonymity, we are no longer able to identify and distinguish a subject within a set of subjects. Anonymizing similar databases would provide an important resource to most universities to explore such data for their management and other research projects. Since these raw data are quite complex, a relevant contribution of our research is to explore effective possibilities to visualize and communicate this information. Previous studies within campuses have examined the dynamics of Wi-Fi and mobile device data. However, they do not consider individual connections and their location. Furthermore, this article aims to contribute to the field exploring possibilities to display and communicate such anonymized large set of information.
Three studies illustrate this data potential in understanding the university’s dynamics and supporting the process of campus planning. Our assumption is that understanding the university’s dynamics will significantly improve planning campus green areas, pedestrian and cycle paths, and new places to enhance interdisciplinary individual encounters. An articulated enterprise should include financing sources, community awareness, new mobility options, and a physical plan based on the ecosystem regeneration associated with places for new encounters. Assuming that the campus has some noteworthy shortcomings, the water streams occupy a strategic position on campus, connecting all surrounding neighborhoods with the university’s territory and the campus itself. Reinforcing the axes of the streams as pedestrians and cycle paths is a fruitful manner to create more innovative and democratic places for the whole community.
Context
Brazilian Federal University of Santa Catarina is in the island Florianopolis. Most of the city area occupies an island which has a great part of its nearly 450 km2 preserved by environmental agencies. The university implementation led a rapid densification of its surroundings resulting in a significant environmental impact for the region. A study from the university verified the development of the basin where the campus is located. Its built area grew from 24.43% in 1998 to 39.52% in 2007 with a result during this period of an increase of around 26% in the volume drained by Meio River. Thus, several floods in the last decades have impacted the campus, causing relevant losses to the university. 1
Inserted in a site of approximately 247 acres in Brazil, Federal University of Santa Catarina’s (UFSC) main campus had its origin in a former farm. It is located in a low region that has collected the water from the surrounding hills before directing it to the nearby mangrove and the sea. As the campus is a few meters above sea level, the floods have always been a concern. 1 Furthermore, the rivers have never been considered as decisive elements in the campus design. Two architects from Sao Paulo developed, in the late 1950s, the first masterplan setting up the basis for the main road axes, and the existing water streams on campus were rectified and channeled as the new roads were constructed. 2
Few constructions and a second masterplan represented a long gap for several years with the university’s managers resisting to invest resources on campus. During the military government, Rudolph Atcon, an educational consultant from the United States, visited the university. 3 Following Atcon’s proposal and the university reform, it started a system based on departments grouped in centers related to knowledge areas. Atcon synthesized his proposal in several diagrams, with the academic centers clearly defined and always a green ring of around 10 m separating and protecting the university from its surroundings. The basic courses should be at the center of the campus, as well as the university administration.4,5
The initial site constructions in the late 1950s and later Atcon’s model were both critical for this article’s argument and have a deep impact on today’s campus. The rectified water streams embody a customary engineering solution for a local problem. Although distributed throughout the campus, with a significant potential to convey important green axes, the rivers were not accepted as aesthetic assets. In several cases, the campus water streams have defined limits to the academic centers, segmenting physical space in different university’s disciplines and constraining interdisciplinary initiatives both in the territory and within the administrative structure. The farthest campus edges have what Atcon suggested as the main interactions with the city: the health center in the north with its recognized hospital (HU) and the southern sports center (CDS/Athletics; Figure 2) affording several facilities open to the community. Thus, there is no need for the neighboring communities to get more than that within the campus. The campus green ring has been an unpopulated space, which separates the other spaces of the university from the neighbors. The fragmented campus does not support innovation through transdisciplinarity or through the city’s interactions.
In contrast, the large roads that cut the campus invite cars to be dispersed throughout its main areas while the water streams and their green edges have acted as obstacles for pedestrians. Although this natural space is protected by the Federal Law no. 7.511/1986,
6
which forbids constructions within 30 m from river’s edges, the university has still considered the campus rivers as drainage channels, covering these edges with parking lots. As stated by Chapman,
7
on the sustainable campus, parking will no longer be a “free good” allowed to consume disproportionate amounts of valuable campus land. By transportation management policies designed to reduce peak parking demand and by consolidation of parking spaces in structures that reduce their footprint, land is given back to higher-value academic and learning support uses and to the restoration of campus landscape. (p. 188)
Campus regeneration
The role of urban universities in the 21st century and particularly their relationship with the cities have raised relevant debates. Jean-Paul Addie 8 reminds that “it’s no coincidence that critical concerns with reclaiming universities’ public mission have risen at the same time as academia’s doors are opened to more entrepreneurial ways of operating.” Our argument is that universities should go beyond acting as economic and social anchors. They should embody and convey ecosystems regeneration associated with social and economic developments within the city. Regenerative design advocates have already extensively raised these issues.9–11 Universities should assume proactive environmental roles highlighting the campus as an example of ecosystem regeneration, demonstrating possibilities to be incorporated by the city.
Bill Reed and his group
12
define regenerative design as a design process that engages the whole of the system of which we are part. Logically, our place—community, watershed, and bioregion—is the sphere in which we can participate. By engaging all the key stakeholders and processes of the place—humans, earth systems, and the consciousness that connects them—the design process builds the capability of the people to engage in continuous and healthy relationship.
When a river is rehabilitated, it is important to count on the positive value impact within the whole community. This shared notion is a central factor in river rehabilitation and preservation. 13 Furthermore, these inherent human values have the potential to conform an aesthetic experience that provokes a more bio-centric perspective, supporting the river’s preservation. 14
In this context, transforming the degraded river ecosystems require a complex association and multidisciplinary initiatives. Public Brazilian universities deal with restricted budget and funding is not usually directed to these issues. Thus, it is crucial to demonstrate that the process of environmental healing may provide gains to both university and city developments. In addition to that, this process allows the university to rethink itself and the way it relates to the environment and the city as a pedagogical tool. Universities have a great possibility in exploring technology to their territory management and to implement more resilient and multidisciplinary facilities and spaces.15,16
The regenerative design process is not restricted to the edge of the rivers and represents an opportunity to engage as a much deeper relationship with life. It is not just a project to a specific area of the campus, it is a regenerative system created through the process of designing the rivers’ regeneration. 12
The rivers’ interventions should not be isolated actions as several conditions and opportunities interact toward this new system. For instance, we could list the parking spaces that occupy important areas within the campus, particularly the rivers’ edges (Figure 1): a new road ring that is currently under construction in an important edge of the university, providing new public transportation systems; the possibility for pedestrians and bicyclists to use the rivers as axes to connect different areas of the campus as well as the campus and the city; pressure from the justice to protect the rivers’ edges; the increasing impact of climate changes and urban densification in floods within the campus; and the expanding traffic problems in the surrounding region.
Parking lots at the river’s edge.
Anonymization and data privacy
Significant amount of data has been collected from the academic community using the university’s Wi-Fi services daily. Each time one performs a connection with the Wi-Fi, a log file is updated with same information such as user’s identification, mac address of the mobile device, and access point, time, and access point location. The Electronic Governance and Information and Communication Technology Superintendence, which is responsible for the university’s network, and the Computer Security Laboratory have provided these data to our experiment. The Superintendence has encouraged anonymization research to make these data available for future research in other areas of knowledge, in order to guarantee the university academic community’s privacy.
In the past few years, several data breaches happened around the world. As example we can cite Yahoo (2014), eBay (2014), Marriot International (2018), and one of the highlight privacy events in early 2018: Facebook data leakage. Most of the data captured was from US citizens. This enabled Cambridge Analytica to conduct analysis of US voters’ data in 2016. The company was able to predict and influence the voters by analyzing their profiles in the social network, helping Donald Trump with a strategic marketing plan based on these data. Another event that was supported by the company was the Brexit Referendum—name given for the impending withdrawal of the United Kingdom from the European Union (EU)—where the Cambridge Analytica supported the official campaign group in favor of leaving the EU. 17
The General Data Protection Regulation (GDPR) is a regulation created in Europe and came into force in May 2018. GDPR is a substitute for the Data Protection Directive 95/46/EC and aims harmonizing data privacy laws through Europe, protecting and enabling data privacy of EU citizens and reshaping the way that data privacy is addressed in organizations. 18 Similar to GDPR, in Brazil, the Federal Senate approved in July 2018 the PLC 53/18, “Lei Geral de Proteção de Dados” (LGPD). The LGPD aims protecting the rights of freedom and privacy of citizens, as Article 1 explains. 19 Both the GDPR and the LGPD require that personal data can only be disclosed if they are pseudoanonymized and follow various instructions in order to ensure privacy. If the data are anonymized, GDPR and LGPD are not applied. 20
Anonymity is considered a state of privacy. 21 To Warekar and Patil, 22 the central idea of anonymization is to ensure that the person is not identified, reached, and tracked. To achieve the anonymity of a subject, it is necessary to have a set of subjects with potentially the same attributes. For an attacker, anonymity means that one is not able to identify the subject within a set of subjects. 23
Since the log file keeps the users’ identification, it is possible to relate it to their data that already exist in university systems (e.g. in the case of students, registration information, their school record, class schedule among other information). This information is known as quasi-identifier. The availability of these data constitutes a valuable research source to understand campus’ dynamics. Therefore, manipulating this information is a central part of how these dynamics are displayed to depict the potential they had.
Similar works have had successful results in using Wi-Fi and mobile device data trace to identify urban dynamics; De Nadai et al. 24 extracted human activity results from mobile phone data to verify Jane Jacob’s four conditions that promote life in a city. Sevtsuk et al. 25 collected, mapped, and analyzed data of Wi-Fi usage in real time on the Massachusetts Institute of Technology (MIT) campus to produce spatial visualizations of the daily working and living patterns of the MIT academic community. They were concerned mainly with network activity and produced maps depicting a holistic sense of user activity on campus. They did not explore data on individual users’ locations. Similarly, for them as well as for De Nadai et al., 24 privacy disclosure was not an issue.
The first part of our methodology addresses how we obtain and anonymize data by grouping all the 300 campus’s access points into 27 campus’s sectors. Most access points are inside the buildings and their signal usually covers an area within a 50-m radius. For this experiment, undergraduate students were grouped in the 11 university’s learning centers. The time interval between the data records was split in 15 min. In addition, every connection on the access point can be used as a point in a path. A point is a pair of coordinates and temporal information. It is possible to aggregate contextual information at each point and create a semantic trajectory because each connection in the access points can be interpreted as a point in a path. A semantic trajectory is a sequence of stops and movements that a person traverses during its path. An example of a semantic trajectory is the sequence of places visited by a subject in motion, such as the library, coffee shop, classroom, university restaurant, and bus stop sequences. 26 These data allow us to track people’s mobility.
A trajectory is described as a discrete sequence of points. These points represent a spacetime evolution of the moving object position. It means that this object is moving in space, during a certain time interval, to reach a certain goal.
Definition 1 (Trajectory): A trajectory is a list of spatio-temporal points, p < x, y,t >,…,pn < xn,yn,tn > where xi, yi ∈ R, ti ∈ R+ for i =,,…, n and t < t < …< tn.
Definition 2 (Semantic Trajectory): Given a list of important places I, a semantic trajectory p, p,…, pn with pi ∈ I is a sequence of important places visited by moving objects.
Semantic trajectories represent the most important places of one’s traveled path. The disclosure of this information may violate the subject’s privacy. Location data allow intrusive inferences which may reveal habits, social behavior, and religious and sexual preferences of individuals. 27 Therefore, data anonymization is critical to the community’s security. The algorithm k-anonymity will be used as a base for the anonymization when the trajectory is not considered. A data set has the property of k-anonymity if there are at least other k – 1 records identical to it for each record formed by quasi-identifiers present in that set. The anonymization algorithm removes from the resulting database any group that does not have at least k subjects. In the last case, which includes the trajectory, a Mix β-k-anonymity anonymizes the data. This work uses a quasi-identifier that represents a group of similar people. A stop is only disclosed if there are at least k – 1 subjects of the same group in a stop within the same time period. A displacement is displayed only if its start/stop has other β moves made by subjects from the same group.
Modeling three-dimensional (3D) data
The research group has developed several proposals and analyses based on a Rhinoceros 3D model of the campus, some of them parameterized with the support of the graphical algorithm editor Grasshopper. The 3D campus model was built using files from different sources, such as .dwg files from the university’s Department of Architecture and Engineering Project and the Municipality. This physical data verification was made by in loco measurements and aerial drone images. The Geographical Information System (GIS) high-resolution digital elevation model (DEM) provided by the State has been matched with the .dwg files for more accurate terrain details. Several video-editing tools have been used for final presentations to different stakeholders.
Rhinoceros and Grasshopper allow reading the CSV files resulted from anonymization algorithms. After the anonymization procedure described above, the data set was systematized and manipulated in order to represent graphically the use and displacement patterns in the university of the groups pre-determined by the chosen quasi-identifiers. The chosen quasi-identifier was one of the 11 university’s educational centers to which each group of undergraduate students belongs. Therefore, we define the population of the learning center by the number of undergraduates linked to the educational network system.
We have explored Wi-Fi connection data with three main approaches: to verify current dynamics and integration potential of the different university centers; the main circulation axes within the campus; and the arrival and departure time of the academic community. For study 1, data were collected on a Wednesday, May 9, 2018. Studies 2 and 3 were performed with data obtained on a Tuesday, August 14, 2018. Each day provided nearly 1.5 million records of undergraduate students.
Study 1: university structure and interdisciplinarity
The fact that UFSC is fragmented in its structure and physical space is generally accepted. However, the administration does not think that a real change is a priority or assumes that their few interdisciplinary initiatives present effective results. If we intend to explore the rivers as relevant drivers for this integration, it is important first to evaluate and highlight the lack of integration, as well as the nuances when this integration occurs.
The anonymization algorithm generated a CSV file with the number of students within the registered group, their learning center, the georeferenced access point location, the university’s sector codes where the access point is located, and the time slot of the connection. To overlap data in the 3D model, Grasshopper was used to read the information in a data tree logic, along with rhinoceros. The item list component allowed using the values as parameters to filtrate and visualize them in the model.
We have considered different possibilities for data representation, such as pin format, spheres, and cylinders. The cylinder conveyed three variables on a small scale, which was decisive for our choice (Figure 2). The cylinders’ color, radius, and height represent different information. Colors display the group’s learning center, radius indicates the center distribution throughout the campus, and the height displays the participation of that center in relation to all connected students in that interval. The dynamics throughout 1 day were registered in a video. Thus, the result is a video that can effectively communicate the information depicted by the cylinders.
Campus’ learning centers and rivers.
A low rate of integration was verified between the different learning centers. This tendency is more evident in the university’s more peripherals areas. The Main Library is the only place where the concentration of students from various educational centers is high and constant. In other locations, the number of students from different learning centers is related to the day period. Greater difference can be verified in the central areas at lunch-time, when diversity increases, particularly in the access points next to the University Restaurant.
Meio River axis connects the north-south university’s longest direction at the central area, while Carvoeira River connects the western edge to Meio River. The rivers’ edges preservation would allow the creation of cycle paths to support longer distances, while pedestrian green walkways generate an enjoyable experience to move to other university areas. New bridges can increase cross-centers interactions, and these new green areas may stimulate inter-center encounters and exchanges.
Study 2: external mobility
The university campus offers nearly 4000 parking spaces, among which many are along the river’s edges, in environment protected areas. The traffic around the university is often chaotic, and the campus is the largest traffic generator in the surrounding neighborhoods. Although some parking spaces are limited to registered drivers, the university is one of the few traffic attractors that does not charge for parking services. Thus, some drivers that do not have the university as destination park their car on campus.
The university encourages the use of private cars. Its parking lots are characterized by mono-functionality, also implying several expenses to afford and maintain these spaces. They represent also a security issue, being among the least safe places on campus. 28 Therefore, controlling, charging, and reducing parking spaces is critical to the protection of the rivers’ edges as well as to improve green public areas in the campus and traffic in the region. Although this decision should be obvious, the university administration finds much opposition from the drivers. The low quality of public transportation is one of the mentioned opposition’s arguments.
The anonymization process delivered a CSV containing the information related to the arrivals and departures on campus, the sector’s geographic coordinates, time interval, the definition whether it was the first or the last connection in the day by the group, and the number of people in the given time slot. The CSV file can be read in Grasshopper with the “Read File” component; its output was split into two lists, arrivals and departures, by using the “Match Text” and “Dispatch” components. The geographic coordinates oriented the cylinders’ location on the 3D model and the amount of people generated the cylinders’ height through the “mass addiction component.”
Figure 3 illustrates students’ arrival and departure time. Considering that most morning classes start at 7:30 a.m., an obvious finding is the concentration of arrivals at this time and a combination of arrivals and departures at 6:30 p.m., due to evening courses. However, one not so obvious finding is the concentration of arrivals at 7:30 a.m. next to the main bus-stops, whereas in the evening, arrivals are more equally distributed between buses and private vehicles (Figure 4).
Cylinders display the number of students from each center (color) connected at 15-min intervals. Cylinder’s height expresses the number of students related to the total connected on campus, whereas the radius illustrates the students’ number in relation to their own center total: (a) the centers’ dynamics at 9:00 a.m. and (b) and at 1:30 p.m., closer to lunch time.
Arrivals and departures defined by first and last Wi-Fi connection in the day: (a) the 15-min interval starting at 7:30 a.m. and (b) at 6:30 p.m.
The city of Florianopolis has one of the smallest off-peak bus usage in the country. Some of the university schedules can be altered, such as the working time of the university restaurant or scheduled classes. These adjustments could move the arrival and departure time to off-peak and transportation companies could offer more buses at a lower cost. Changes should be timed with parking control stimulating decisions in transportation modes, from private individual cars to public buses. Increased demand influences improvements to public transportation while fewer cars improve city traffic, campus public spaces, and, ultimately, rivers’ quality.
Our study may provide useful and verified data for the bus company to improve public transportation. The university population corresponds to 10% of the municipal inhabitants and may be the majority of bus users. Queries for the Wi-Fi database should focus on users’ arrival and departures time and users’ destination. The location where they connected for the first and last time is important to facilitate the decisions in where to place a bus station or transport hubs.
A further study should be produced to associate users’ identification with their addresses, without threatening their privacy. This study will complement a questionnaire to identify how likely each user may change one’s transportation mode. Even if we still cannot relate their addresses to arrival and departure connections, it is still possible to identify the percentage of residents in different areas of the city and connect it to the questionnaire.
Study 3: internal mobility
As mentioned before, the parking spaces and motorized vehicles take over most of the important free areas at the campus, mainly along the river’s banks, where several of the pedestrian’s trajectories could easily occur. In addition to that, there are significant opportunities for resilient projects on these places, based on solutions that respond adequately to extreme events, especially floods. Rivers are defined by their continuity as ecosystems “roads”; they associate open and enclosed spaces and may act in favor of a biophysical integration and preservation of its perceptual meaning. While contributing to the ecological quality of an environment, they can improve the relationship by establishing better connections among the different university’s sectors and the city in its surrounding. Furthermore, it is critical that they assume a leading role in resilient projects on campus. 29
To identify construction priorities or new pedestrian or cycle paths, this study analyzed the usual trajectories that students from different centers perform throughout the campus and when they occur. The proposed algorithm anonymizes the data of a group in the daily granularity and saves the log of connections. Time bands represent the generalization of the connection time to an access point; these time bands group temporal information to reduce the possibilities of user’s identification; data are organized by groups, place, and time range. The number of people from a certain group, who were in the same place and in a certain time range, is represented by x. The algorithm receives a list of trajectory points from people of the same group. Each point contains the following attributes: id, location name, time, time range, latitude, longitude, number of people who were also in this location within this time range, user, an empty list of next points, and a variable of type Boolean named as grouped starting with the false value. Choosing the learning center as a quasi-identifier opposed to departments, for example, guarantees a larger number of individuals in the same group minimizing the chances of missing data by the anonymization algorithm and increasing the disclosed trajectory data quality.
The geographical coordinates where the first and last connections occurred are extracted by the “Tree Branch” component and reorganized into two point lists. This information continues to be linked by its storage index, allowing later to connect the points precisely through their respective origin and destination with the line component, generating the semantic trajectory of each group. The different groups can be identified by colors that correspond to their respective teaching center. These semantic trajectories are represented on the model with constant thickness and reduced opacity. As some semantic trajectories overlap in the representation, the line gains more opacity, demonstrating the recurrence of the path, thus indicating more importance in the system. The cylinder height is proportional to the number of students from each center in a given sector and time interval. Blue represents the Technology Center, yellow the Center for Communication and Expression, red the Health Center, and green the Philosophy and Humanities Center (Figure 5).
Lines represent groups of students’ trajectories, separated by centers, and cylinders illustrate number of students from the center at the sector within a 15-min interval: (a) the time interval starting at 9:00 a.m. and (b) at 10:00 a.m.
Through the trajectory analyses across the campus, we could identify that the most used pedestrian paths have a significant low walkability. On the contrary, most of the university’s rivers can establish meaningful paths for many of these academic community’s connections. The increased quality of these paths will induce more circulation and stops, stimulating more diverse encounters. Chapman 7 considers the campus as “the agora of the twenty-first century, made up of the formal and informal places where people can cross paths” (p. 194). With the expanding offer of online courses, personal encounters that may promote creative and innovative exchanges are one of the most important campus features. Therefore, demonstrating this added value to the university administration facilitates the project’s economic viability.
Meio River is the longest river within the campus, establishing relevant connections with the city on the north–south axis. However, the results examined have highlighted Carvoeira River that enters at the university’s western border and ends at Meio River. In fact, it concentrates most of the students’ connections. However, the quality condition of several of them is very low, usually crossing parking lots. These findings demonstrate to the university administration the benefits to prioritize them. In addition to that, busier trajectories suggest best places for bridges, improving circulation and experience when crossing the rivers. Aiming for the rivers’ resilience supports the area resignification, overcoming the rivers’ associated message as obstacles to pedestrians and threats during the flood seasons.
The rivers present great opportunities to increase interactions with the closest neighborhoods. Thus, although our data are only related to the academic community, our study may reach beyond the campus’ borders. Bringing more people close to environmental regeneration initiatives support better conservation and public awareness. 30 These findings demonstrate to the university administration the benefits to prioritize them.
Conclusion
Effective data visualization is a key issue to communicate available complex information to administrative instances. A relevant challenge is to associate this visualization to a campus 3D model that may also convey future projects. The research group has explored other software to display such information, like QGIS and ArcGIS. ArcGIS offers powerful tools for mapping and visualizing georeferenced data connected to external spreadsheets, such as Microsoft Excel. However, Rhinoceros and Grasshopper proved to be more adapted to represent design proposals in videos associated with dynamic external spreadsheets. We have tried several alternatives for representing dynamic data. Adjusts have been performed to make changes clearer over time facilitating to communicate the results in video representation. Videos are powerful tools to convey such dynamic data to larger and diverse audiences and have been extensively used in this study.
Technology has changed and shaped the way in which people live and inhabit cities in contemporary societies. As stated by Adam Greenfield, 31 “networked digital information technology has become the dominant mode through which we experience the everyday. In some important sense this class of technology now mediates just about everything we do.” Greenfield concerns reside in the fact that these same devices we have used to map people’s dynamic in this study are also responsible to avoid an unplanned conversation with the nearest person. However, our aim of using technology has addressed the opposite direction. Promoting green spaces next to rivers facilitates unexpected interdisciplinary encounters, not mediated by technology. We believe that the future of the campus is directly related to the quality of those encounters for they should stimulate creative and innovative initiatives, defying Atcon’s fragmented structure and providing new layers of knowledge exchange.
The University noteworthy Wi-Fi connections database is a powerful tool, still overlooked by the administration. A regular day provides millions of records that register the student identification with location and time. Student identification constitutes a piece of very sensible information. Governments are aware and have responded with more strict legislation to guarantee data privacy. Our research group has examined possibilities to manipulate this database assuring the data quality and user anonymization. These data may identify and support integration among internal university instances and with external stakeholders, such as the municipality, transport company, and many others. Regenerative design requires the association of ecosystems regeneration with all key stakeholders and other processes of the place. These data may change priorities, such as current postponing of resilient projects that may eliminate future university losses, reduce costs or promote university’s economic cycles, and generation gains that are not immediately evident.
Human dynamics should be seen next to other environmental dynamics. The transdisciplinary design process requires new approaches as well as proper analysis and communication tools. If we consider, for example, resource scarcity, mobility problems, structural and physical deficiencies, and universities still tied to old conception models, we need to change the decision-making paradigm. Really resilience requires broad solutions to generate positive impacts in a reaction chain. How long does it take to regenerate most rivers on the island, in order to stop pollution in the bay? In a city based on motorized individual transportation, can we have a campus without cars? If the campus is that important to the city, is it possible to influence the whole island to have public and active transportation as its mobility base?
Footnotes
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil (CAPES)—Finance Code 001.
