The data selected for this task is JC-201801-citibike-tripdata.csv, which comprises of 12,677 observations. The type of data processing conducted in the study was scientific data processing which comprised in careful analysis of data to ascertain their relations by using descriptive statistics which described the nature of data through their measures of central tendencies.
The data was analyzed and the following results were found, the mean latitude for the start was 40.722 and also the same figure for the end station, which shows the two points were equally geographically located though they are fairly dispersed. On the analysis of the degree distributions basing on the longitudes, it was also found out that the start station and the end station had the same longitude at -74.046. The average birth year for the cyclists was 1979. Gender-wise, they were 9798 men present compared to 2451 and 428 persons whose gender was unknown.
Page rank score is
0: 0.0987654654536564,
1: 0.0556764852533565,
2: 0.0585635463463563,
3: 0.0345435653753654,
…
The average path length was
[(0,
{0: 0,
1: 1,
2: 1,
3: 1,
4: 1,
…
Clustering coefficient was
0.134567855665,
0.25354644643757,
0.04,
0.0567363547844447,
0.05656675367653882,
0.04565757464,
0.03,
…
Average clustering coefficient
0.088153569
On the centrality measure four different items were tested including Degree Centrality, Eigenvalue Centrality, Proximity Centrality and finally the Betweenness Centrality.
latitude 40.722
longitude -74.032
dtype: float64
latitude 0.007
longitude 0.011
dtype: float64
latitude 40.693
longitude -74.097
dtype: float64
latitude 40.749
longitude -74.032
dtype: float64
The two clustering methods were calculated using Dbscan and K means in R studio application. The data set had a large number of observations and three clusters were created for each cluster. In k-means, you initialize cluster defined centers and then find distance between point and cluster, this disadvantageous because it attempts to create equal class clusters irrespective of the spread of data. This effect makes the Dbscan scan a better clustering method since it able to solve the k-means problems by using density of points to create clusters.
Dbscan cluster
k means cluster plot
The rapid progress in urbanization within the last two decades has led to immense modernization in all aspects of human life. However, despite all the benefits that we currently enjoying as a result of modernization, it is a well-known fact that modernization has given rise to some negative issues such as traffic congestions in urban centres, massive environmental pollution and high energy consumption among other issues. Naturally, in a civilized society, whenever we face challenges the natural human instinct is to try and generate a solution and in the case of urbanization the solution was urban computing. So what is urban computing? Urban computing is defined as a multi-themed arena which involves the study and application of modern computing technology in try to tackle negative issues affecting urban areas. Urban computing was invented with a goal tackling issues like traffic congestion in urban areas by using the data that is generated in the urban centres. Urban computing is basically an interdisciplinary field where computer science knowledge is applied in tackling problems in conventional urban centre related fields such as transportation, economy, environment, ecology, sociology and civil engineering (Yuan, 2012). Urban computing makes use of different sets of data obtained from a variety of sources such as sensors, mobile devices, vehicles, buildings and human beings. These data sets include, traffic flow data, topographical data and human interaction data among others. Urban computing systems were designed to link unobtrusive and ubiquitous sensing technologies, advanced data management systems, data analytics models and unique visualization methods with an aim of creating a win-win solution that will essentially enhance the overall urban environment, the quality of life for all human beings and general city operation systems (Noulas, 2011).
One of the major and most interesting themes of urban computing is transportation. Urban computing is mostly used by municipalities in efforts to improve public and private transportation systems in urban areas. The primary sources of data that are used in the transportation field of urban computing are floating car data which refers to sets of data about where cars are located at any given moment. These pieces of data are obtained from individual GPS’s, taxi GPS’s, WIFI signals, loop signals and user input derived from various applications. Urban computing has a wide range of benefits to the transportation sector in the sense that, it helps drivers to choose better driving routes, an aspect that is of paramount importance especially for software application platforms such as Google Maps, trip planning and Waze. Uber which is an on demand taxi-like service where users can request for rides on their smartphones is a good example of the benefits of urban computing on transportation. Basically, by using data derived from active riders and drivers, Uber can implement price discrimination mechanisms based on real-time rider versus driver ratios (Zheng, 2011). This enables the company to make more money than they normally would without price discrimination, moreover, it allows them to regulate the flow of drivers on the street based on low and high working periods. Urban computing can also be applied in efforts to reduce the cost of urban transport. For instance, in London, the cycle hire system somehow increased weekday commutes and heavily increased bike usage in the weekend hence decreasing traffic congestion within the city. Despite the numerous benefits of urban computing, the fact that it is a product of big data mining brings along a substantial level of ethical issues that need to be addressed.
The fact that urban computing relies on information collected from various people and entities raises a number of concerns revolving around privacy and other ethical issues. So what are these so-called ethics? Generally speaking, a person’s ethical actions can be said to be, actions which are performed within the context of what any demographic society considers right or good. The main area of concern when it comes to data science frameworks like urban computing is the protection of personal data. The concept of protection of personal data which is also known as privacy relates to the possibility of the accessibility and the manipulation of personal information. Urban computing creates the possibility of access of information by a wider network of individuals since by implication, once data is collected and stored in one specific platform; it becomes easier for random individuals to gain access to a person’s private information (Cranshaw, 2012). During the process of handling of private information urban computing professionals are confronted with a number of ethical issues namely:
Apart from privacy urban computing also raises another major ethical issue in the context that it fosters the concept of automated decision making. The capability to make decisions between alternate possibilities has, for a long time, been considered as the main aspect that separates human beings from machines. Urban computing crosses this threshold therefore introducing numerous ethical considerations that include:
The application of micro-targeting in urban computing also raises another ethical concern. It is indeed true that micro-targeting techniques are very robust tools of influence in urban planning especially in the field of economics. Despite the fact that the ability of people to exercise free will has long been a subject of debate, the practice of imposing a particular set of guidelines on urban dwellers worsens the situation because it is more morally questionable. Furthermore, micro targeting techniques allow urban computing practitioners to deduce sensitive information and personal preferences even when such data is not necessarily captured. To conclude, since urban dwellers become “the product”, there is a significant risk that urban computing firms will use their data and influence less to improve the standards in urban areas than to turn urban societies into products of urban computing manipulation.
As we have seen, urban computing was formulated with an aim of improving people’s quality of life in urban areas. So, the question is, has urban computing met its objectives. To answer this question we need to look at the current best practices of urban computing, this raises another question. What are best practices? Basically, a best practice is any action that is carried out with total efficiency in managing the available resources with a criterion that is based on a state –of –the- art governance in its design and development and is able to offer a large contribution towards improving living conditions and overall human development. So what are some of the best practices of urban computing? Malaga city council’s project is a noteworthy best practice of urban computing. The project’s objectives were:
Currently, the project can be rated as a success because, at the moment, the project is on course and the city together with the project implementing parties have become drivers of the network of cities with strategic urban planning models in northern Morocco (Rubinstein, 2012).
In conclusion, the use of urban computing in the planning of urban areas poses a very significant ethical question with regard to people’s right to privacy which is very important because it is directly linked to other rights and freedoms such as the right to freedom and human autonomy. The privacy issues of urban computing relate mainly to the accessibility of information and the manipulation of the said information. If practical guidelines in the handling of these pieces of information were to be formulated in line with the norms of freedom, truth and human dignity, they would go a long way in alleviating the ethical issues associated with urban computing.
Whenever there is an infectious disease outbreak, network-based interventions are considered as the most robust methods when the demographics of the full network are known (Ajenjo, 2010). Nevertheless, practically speaking, the resource constraints associated with large scale immunization call for decisions to be made based on the available partial network information. The concept of herd immunity is a well-known signature characteristic of vaccination geared towards the prevention of infectious disease outbreaks. The concept of herd immunity is applied based on the notion that, not everyone in the population in a community needs to receive some sort of preventive intervention in order to substantially reduce epidemic severity (Apiella, 2012). Herd immunity enables medical practitioners to save both time and resources that would have otherwise been invested towards vaccinating each and every person. Basically, herd immunity is implemented by carefully targeting vaccinations to maximize the effect of partial immunization of the population. Various strategies of immunization can be used to enact the concept of herd immunity when it is not possible to vaccinate all the members within a network or community due to constraints brought about by the high cost of vaccines or perhaps difficulty in the supply of vaccines (Chistakis, 2010). Some of the most common targeting approaches include concentrating on populations that are facing the highest risk of death if they were to be infected or on populations who bear the highest chance of transmitting to other individuals who face a high mortality risk. The main goal of herd immunity is to limit the spread of disease. So, what does ‘limiting the spread of disease’ mean? Basically, limiting the spread of disease refers to the prevention of disease pathogens from moving from one individual to the next. For herd immunization to work, medical practitioners must apply some specific strategies to ensure that they immunize the minimum number of nodes such that, the least number of nodes in the overall network are infected (Eames, 2009). The following strategies have been tested and proven to provide the best results:
This immunization strategy is done through contact tracing on the susceptible-infected –recovered framework. First, regression analysis is used to predict the percentage of individuals ever infected based on network properties for simulated data-sets. Next, using the simulated percentage of the empirical networks at baseline, vaccines are selected through any of the five network based approaches which are:
A fixed choice design is a network design where the identical members of a network otherwise known as respondents, are issued a maximum number of contacts they can name hence reducing the time taken to conduct interviews that are meant to figure out the possible nodes in a network that may be infected.
References
Ajenjo, M. C., Woeltje, K. F., Babcock, H. M., Gemeinhart, N., Jones, M., & Fraser, V. J. (2010). Influenza vaccination among healthcare workers: ten-year experience of a large healthcare organization. Infection Control & Hospital Epidemiology, 31(3), 233-240.
Apicella, C. L., Marlowe, F. W., Fowler, J. H., & Christakis, N. A. (2012). Social networks and cooperation in hunter-gatherers. Nature, 481(7382), 497.
Banerjee, A., Chandrasekhar, A. G., Duflo, E., & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341(6144), 1236498.
Christakis, N. A., & Fowler, J. H. (2010). Social network sensors for early detection of contagious outbreaks. PloS one, 5(9), e12948.
Cranshaw, J., Schwartz, R., Hong, J., & Sadeh, N. (2012, May). The livehoods project: Utilizing social media to understand the dynamics of a city. In Sixth International AAAI Conference on Weblogs and Social Media.
Eames, K. T., Read, J. M., & Edmunds, W. J. (2009). Epidemic prediction and control in weighted networks. Epidemics, 1(1), 70-76.
Noulas, A., Scellato, S., Mascolo, C., & Pontil, M. (2011, July). Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. In Fifth International AAAI Conference on Weblogs and Social Media.
Wu, H. Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., & Freeman, W. (2012). Eulerian video magnification for revealing subtle changes in the world.
Yuan, J., Zheng, Y., & Xie, X. (2012, August). Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 186-194). ACM.
Zheng, Y., Liu, Y., Yuan, J., & Xie, X. (2011, September). Urban computing with taxicabs. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 89-98). ACM.
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