Data_150_02_S21_Jack_Hayes

Research Plan

Word Count: ~1900

Introduction

Gender inequalities and inequities have been affecting the world seemingly since the beginning of time. Especially with a recent surge in social justice, the battle against gender perspectives around the globe has gained more and more attention. However, many countries, like Japan, are still struggling to combat their modern gender roles. These gender inequities in Japan stemmed from a period of high economic growth after World War Two, in which pretty typical gender roles arose. One of the main sources of these gender-related issues in Japanese society is not as apparent as one would think, however. Data concerning the reinforcement of gender perspectives can be seen in almost every natural disaster in Japan’s recent history. For example, women have a significantly higher mortality rate than men in Japan due to unequal economic opportunity and have to constantly voice their grievances about being unheard in their communities during disaster reconstruction. This unique problem is extremely prevalent during the almost never-ending process of reparation in Japan. For example, the Great East Japan Earthquake occurred in 2011, and the social discord caused by the earthquake is still felt today, as reconstruction continues. The social cohesion of a population leads to a better educated, economically flourishing, happy society, but social cohesion cannot be achieved without the recognition and solving of gender inequities.

Graph displaying the frequency of Japan’s earthquakes since 2016 (https://www.nippon.com/en/ncommon/contents/japan-data/203435/203435.png)

Solving this abnormal problem is no easy feat, but could lead to a great improvement of human development not just in Japan, but in many other nations across the world. Luckily, some case studies relating to this distinctive problem have produced significant results to help with disaster response. It was found through satellite-gathered data and surveys that ideas such as group relocation, temporary shelters, and gender equality centers all have noteworthy effects on increasing social development and community resilience during times of disaster. To dive deeper into this matter, an evaluative and explanatory inquiry was crafted relating to the effects natural disasters have on social development and community resilience: What policies and ideas can Japan’s authorities implement within their disaster management systems to combat gender perspectives and improve community resilience as a whole? (JST/NSF, 2013) (Ogata, 2016) (Science Advances, 2017)(Saito, 2014) (Social Work, 2018).

To answer this problem inhibiting the human development of Japan, a three step research plan was developed. The first step is to collect more CDR, or call detail records, data. CDR data is one of the largest methods in geospatial data collection that will be explained later. This type of data is hard to gather in large quantities since it’s such a recent method, so the big corporations who collect and have access to this type of data charge money for the acquisition of said data. The next step would be to utilize this data with other methods. Comparing the behavioral patterns and displacement measurements captured CDR data with data from satellite-based information and surveys would provide an even better understanding of social development and community resilience after crises, which would then lead to the third step: Being prepared for Japan’s next earthquake. Unfortunately, another earthquake in Japan is inevitable, but preparedness is paramount for the process of reconstruction, both socially and physically. We already understand what individual ideas benefit community resilience in disaster management systems, but seeing all of these methods work together in action will lead to more significant conclusions. Showcasing how an improved disaster management organization greatly improves the human development and social flourishment of Japan will influence Japanese authorities, and authorities around the world, to follow in these footsteps.

Call Detail Records

To understand the first two parts of the research plan, it is important to understand what CDR data is, and what metrics within the data are most useful when it comes to disaster response. CDR refers to the mobile phone call detail records that track individuals. As one might guess, this is incredibly useful when inspecting the behavioral activities and patterns of people after a crisis. CDR analysis is an efficient method for tracking large numbers of people in a meaningful way without interview bias, but there are some inconsistencies in the collection of CDR data. Unfortunately, CDR data are almost never representative of an entire population, because only people who can afford and use SIM cards can be tracked. Especially in less developed countries, there can be a big problem of underrepresentation. Despite these negatives, CDR data is extremely useful when used as a complement to traditional methods. When looking at post-disaster resilience within a population, CDR-based analyses are very useful, as understanding the behavioral patterns of a population leads to more complete methodologies concerning the improvement of disaster response. The two metrics important for this research are the radius of gyration and entropy (Flowminder Foundation, 2019).

Radius of Gyration

The radius of gyration is normally defined as “the standard deviation of the distances between each visited location and the centroid, over a specified time period”(Flowminder Foundation, 2019). The typical equation for this is: . The length of the time period is n, while rc is the centroid, calculated as the mean location based on the frequency of visits. Since standard deviations are skewed by unusually high values, long-distance trips are heavily weighted in this method. Additionally, if a small sample is being analyzed, using the absolute deviation is more accurate than the standard deviation. When plugging in the variables to this equation, one must find the mean distance from “home” every week as the centroid, calculate the absolute value of the difference between every daily location and its corresponding centroid, and calculate the average of the weekly absolute differences. This is the methodology used to find the true radius of gyration, which assumes all locations lie along a one-dimensional line. For inspecting changes in pre- and post-disaster periods, however, a reduction in complexity is needed. A team of researchers in a recent 2019 study on estimating disaster resilience did just that. By analyzing migration behaviors from the 2010 Haiti Earthquake, 2016 Hurricane Matthew, and 2011 Nepal Earthquake, the team observed that many relocation displacements occur over distances under a few kilometres. Because of this, the researchers developed a modified RoG that is more sensitive to short trips. Taking the logarithm of the modulus of the difference between daily locations and their centroids and calculating the mean of the weekly logarithmic differences ensures that short-distance travel is weighted heavier. This logarithmic radius of gyration (LRoG) is incredibly beneficial for assessing behavioral and migratory patterns after a disaster. The team found that using their modified radius of gyration and entropy metrics(which will be explained later), the resettlement rates, or time it takes for an individual to find “home”, in all three of the natural disasters investigated could be modeled as exponential decay curves. A better understanding of these migratory activities leads to improved disaster response planning for the future, a greater analysis of the social effects of a crisis, and finer data in general for conducting more studies relating to disaster resilience(Flowminder Foundation, 2019).

Entropy

In general, entropy quantifies the predictability of an individual’s movements, but there are several definitions of entropy depending on certain sensitive behaviors specific to certain events. True entropy looks at the probabilities of all subsequences within a time series and accounts for a relative frequency and order of visited locations. Temporal-uncorrelated entropy only accounts for a relative frequency and random entropy only depends on the number of distinct locations visited.

Like radii of gyration, however, these types of entropy can be modified to benefit the analysis of post-disaster migration. The team in the previously mentioned 2019 study also created an entropy-based metric sensitive to changes in location, but independent of distance. This new “step entropy” is based on Boltzmann’s entropy in statistical thermodynamics. In this metric, consecutive visits to the same location are reduced to a singular visit, and the locations visited are grouped as a sequence. The entropy is then defined as the logarithm of the distinct permutations of the total numbers of visits in said sequence and the total number of times a specific location occurs in the sequence.

To sum it up, this metric is simply a measure of disorder. The reason this type of entropy is so important when it comes to disaster management is because of how fast it can display migratory patterns when individuals are highly mobile. This is incredibly useful after an earthquake, for example, when individuals are constantly moving from temporary shelter to temporary shelter in search for “home”(Flowminder Foundation, 2019).

Gaps and Objections

One of the biggest gaps in this research is the actual implementation of methods to help improve disaster management systems. Yes we know that ideas like group relocation and gender equality centers help community resilience after a disaster, but knowing how to best implement all of these ideas together is another question. The utilization of CDR data will be used to address this gap. By comparing CDR data to data from previous case studies and findings, one will be able to create a more detailed system of the implementation of said ideas for disaster response. Some argue that spending large amounts of money for the access of CDR data is a waste, and that the money should be put into the actual disaster management system instead, using the data we already have. Although CDR data is relatively expensive, temporary shelters are already provided in large quantities by Japan’s disaster management system, group relocation doesn’t cost any money, and gender equality centers are relatively cheap to run. Therefore, putting more funds into CDR data will lead to better overall preparedness for Japan’s next disaster, and understanding the best qualities of an infrastructure is more significant than randomly throwing a couple more structures in the mixing pot. The progress of human development in nations is too serious a matter to be continuously guessing and checking. This research plan ensures preparedness and confidence in solving this unique, goliath of a problem.

Graph of earthquake predictibility in Japan in the near future (https://www.nippon.com/en/ncommon/contents/features/52037/52037.png)

Budget Plan

Regarding the budget plan, the funds will mainly be used for obtaining past CDR data and running a case study on Japan’s next disaster. Unfortunately, I believe that the CDR data collected during the Great East Japan Earthquake is a little too biased, as the disaster occured in 2011 when cell phones weren’t as prevalent. Despite this, there are still many disasters around the world to pull from. For example, CDR data from the Nepal earthquake of 2015 and from the 2018 Hokkaido Eastern Iburi earthquake would be two of the main disasters to focus on. Although Nepal’s society and infrastructure is different from Japan’s, the process of reconstruction and disaster management after an earthquake is very similar no matter the nation. Contacting those involved with CDR case studies for these disasters and obtaining the data will provide a great amount of desired complement information. Concerning setting up a case study for Japan’s next earthquake, funds will be needed to hire a team to conduct surveys, implement ideas, and gather satellite data to compare with previous disaster responses in the nation. I truly believe that significant results regarding human development and disaster response efficiency will be drawn from this study. Proving that a great disaster management system helps suppress social discourses like gender perspectives and improves the overall development of a nation will have a great effect on other countries that also struggle with natural disasters. Hopefully, the results will set off a chain reaction of altering disaster management organizations around the world, thus increasing the level of human development globally.

Sources and References:

Anderson, K., Aoi, S., Pu, C., Tu, G. (2013). Big Data and Disaster Management A Report from the JST/NSF Joint Workshop (Publication).

Geospatial World & Media!, Yoshikawa, K., Takagishi, S., Okajima, Y., PASCO Corp. (2016, May 03). Japan earthquake disaster assessment using RS tech. Retrieved March 05, 2021, from https://www.geospatialworld.net/article/japan-earthquake-disaster-assessment-using-rs-tech/

Hikichi, Hiroyuki, Yasuyuki Sawada, Toru Tsuboya, Jun Aida, Katsunori Kondo, Shihoko Koyama, and Ichiro Kawachi. “Residential Relocation and Change in Social Capital: A Natural Experiment from the 2011 Great East Japan Earthquake and Tsunami.” Science Advances 3, no. 7 (July 1, 2017): e1700426. https://doi.org/10.1126/sciadv.1700426.

Li, T., Dejby, J., Albert, M., Bengtsson, L., & Lefebvre, V. (2019, August 06). Estimating the resilience to natural disasters by using call detail records to analyse the mobility of internally displaced persons. Retrieved from https://www.flowminder.org/resources/publications/estimating-the-resilience-to-natural-disasters-by-using-call-detail-records-to-analyse-the-mobility-of-internally-displaced-persons/

Ogata T. (2016). Disaster Management in Japan. Japan Medical Association journal : JMAJ, 59(1), 27–30.

Saito, Y. (2014). Progress or repetition? gender perspectives in disaster management in japan. Disaster Prevention and Management, 23(2), 98-111. doi:http://dx.doi.org.proxy.wm.edu/10.1108/DPM-08-2013-0134

Voigt, Stefan, Giulio-Tonolo, Fabio, Lyons, Josh, Kučera, Jan, Jones, Brenda, Schneiderhan, Tobias, . . . Guha-Sapir, Debarati. (2016). Global trends in satellite-based emergency mapping. Science (American Association for the Advancement of Science), 353(6296), 247-252.

Yoshihama, Mieko, & Yunomae, Tomoko. (2018). Participatory Investigation of the Great East Japan Disaster: PhotoVoice from Women Affected by the Calamity. Social Work (New York), 63(3), 234-243.