Enhancing Trip Distribution Using Twitter Data: Comparison of Gravity and Neural Networks

Predicting human mobility within cities is an important task in urban and transportation planning. With the vast amount of digital traces available through social media platforms, we investigate the potential application of such data in predicting commuter trip distribution at small spatial scale. We develop back propagation (BP) neural network and gravity models using both traditional and Twitter data in New York City to explore their performance and compare the results. Our results suggest the potential of using social media data in transportation modeling to improve the prediction accuracy. Adding Twitter data to both models improved the performance with a slight decrease in root mean square error (RMSE) and an increase in R-squared (R2) value. The findings indicate that the traditional gravity models outperform neural networks in terms of having lower RMSE. However, the R2 results show higher values for neural networks suggesting a better fit between the real and predicted outputs. Given the complex nature of transportation networks and different reasons for limited performance of neural networks with the data, we conclude that more research is needed to explore the performance of such models with additional inputs

A future agenda for research on climate change and human mobility

In the past 15 years, research activities focusing on the interlinkages between climate change and human mobility have intensified. At the same time, an increasing number of actors and processes have sought to address human mobility in the context of climate change from a policy perspective. Hitherto, research has been limited in terms of geographical preferences as well as conceptual and methodological focus areas. This paper argues that to address the evolving policy space, future research on climate change in the context of human mobility needs to become more differentiated, integrated and generalized. This includes concerted efforts to better integrate researchers from the global South, improved cross‐linkages between different datasets, approaches and disciplines, more longitudinal and comparative studies and development of innovative qualitative and quantitative methods

A future agenda for research on climate change and human mobility

In the past 15 years, research activities focusing on the interlinkages between climate change and human mobility have intensified. At the same time, an increasing number of actors and processes have sought to address human mobility in the context of climate change from a policy perspective. Hitherto, research has been limited in terms of geographical preferences as well as conceptual and methodological focus areas. This paper argues that to address the evolving policy space, future research on climate change in the context of human mobility needs to become more differentiated, integrated and generalized. This includes concerted efforts to better integrate researchers from the global South, improved cross-linkages between different datasets, approaches and disciplines, more longitudinal and comparative studies and development of innovative qualitative and quantitative methods.</p

Spatiotemporal Modeling in Wireless Communication Networks

تهدف هذه الدراسة إلى تحليل تدفق هجرة الأفراد بين المحافظات العراقية باستخدام بيانات مجهولة حقيقية من شركة كورك تيليكوم في العراق. الغرض من هذا التحليل هو فهم بنية الاتصال وجاذبية المدن أو المحافظات من خلال فحص هجرة التدفق والكثافة السكانية، لذلك من وجهة النظر هذه يتم تصنيفها على أساس الهجرة البشرية في وقت معين. تمت ملاحظة بيانات الهاتف المحمول من نوع المكالمات التفصيلية للمكالمات ((CDRs، والتي تقع في فترة 6 أشهر خلال COVID-19 في العام 2020-2021. وفقًا لطبيعة CDRs، تم تطبيق الخوارزميات المكانية والزمانية المعروفة: نموذج الإشعاع ونموذج الجاذبية لتحليل هذه البيانات، واتضح أنها مكملة للآخر بناءً على النتائج التي تم الحصول عليها. تم تمثيل النتائج من خلال استكشاف التدفقات لكل محافظة على مستويين من التجريد: الماكروسكوب والميزوسكوب. وجدت النتائج أن نماذج التفاعل الزماني المكاني مكملة للآخر، حيث تم حساب التدفقات بواسطة نموذج الإشعاع الذي سيتم استخدامه في نموذج الجاذبية. كما تم الحصول على ملخص للتدفقات بين المحافظات ولكل محافظة على حدة. واستناداً إلى العينة المأخوذة من إجمالي عدد التدفقات، كانت أعلى نسبة جذب بين محافظتي نينوى وذي قار وبلغت٪ ، بينما كانت أقل نسبة جذب بين محافظتي واسط وكربلاء والتي بلغت   . بالإضافة إلى ذلك، أظهرت الخرائط النسبة المئوية لكل محافظة، في إشارة إلى لون كل محافظة، من اللون الفاتح الذي يعني انخفاض الجذب، إلى الغامق الذي يعني الجذب العالي. في المستقبل، من الممكن الحصول على بيانات أكثر تفصيلاً واستخدام خوارزميات الشبكة المعقدة لتحليل هذه البيانات.This study aims to analyze the flow migration of individuals between Iraqi governorates using real anonymized data from Korek Telecom company in Iraq. The purpose of this analysis is to understand the connection structure and the attractiveness of these governorates through examining the flow migration and population densities. Hence, they are classified based on the human migration at a particular period. The mobile phone data of type Call Detailed Records (CDRs) have been observed, which fall in a 6-month period during COVID-19 in the year 2020-2021. So, according to the CDRs nature, the well-known spatiotemporal algorithms: the radiation model and the gravity model were applied to analyze these data, and they are turned out to be complementary to each other. However, the results explore the flows of each governorate at two levels of abstraction: The Macroscopic and Mesoscopic. These results found that the spatiotemporal interaction models are complementary to the other, as the determined flows based on the radiation model have been used in the gravitational model. Furthermore, flows summary among all the governorates as well as for each of them has been obtained separately. Thus, based on the total number of flows, the highest attraction rate was between Nineveh and Dhi Qar governorates which reached , while the lowest attraction was between Wasit and Karbala governorates which reached . In addition, the extracted geographical maps showed each governorate ratio. Regarding the color of each governorate that degraded from light to dark, which indicated the low to high attraction respectively. In the future, it is possible to obtain more detailed data, and to use complex network algorithms for analyzing this data

Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information

The movements of individuals within and among cities influence key aspects of our society, such as the objective and subjective well-being, the diffusion of innovations, the spreading of epidemics, and the quality of the environment. For this reason, there is increasing interest around the challenging problem of flow generation, which consists in generating the flows between a set of geographic locations, given the characteristics of the locations and without any information about the real flows. Existing solutions to flow generation are mainly based on mechanistic approaches, such as the gravity model and the radiation model, which suffer from underfitting and overdispersion, neglect important variables such as land use and the transportation network, and cannot describe non-linear relationships between these variables. In this paper, we propose the Multi-Feature Deep Gravity (MFDG) model as an effective solution to flow generation. On the one hand, the MFDG model exploits a large number of variables (e.g., characteristics of land use and the road network; transport, food, and health facilities) extracted from voluntary geographic information data (OpenStreetMap). On the other hand, our model exploits deep neural networks to describe complex non-linear relationships between those variables. Our experiments, conducted on commuting flows in England, show that the MFDG model achieves a significant increase in the performance (up to 250\% for highly populated areas) than mechanistic models that do not use deep neural networks, or that do not exploit geographic voluntary data. Our work presents a precise definition of the flow generation problem, which is a novel task for the deep learning community working with spatio-temporal data, and proposes a deep neural network model that significantly outperforms current state-of-the-art statistical models