Rapid and uncontrolled changes in land use patterns due to urbanization negatively affect urban rainfall-runoff processes and flood hazard. In this study, a method that included different sustainable drainage solutions, such as green infrastructure (GI) usage for flood hazard mitigation with various scenarios on a geographic information system (GIS) platform within a 1653 ha catchment of the Kağıthane Stream in İstanbul, Turkey is presented. Developed scenarios are as follows: scenario one (SN1) is the current situation; scenario two (SN2) used green roof application for buildings and a permeable surface for roads; scenario three (SN3) used only green roof application for buildings; scenario four (SN4) used a rainwater barrel for collecting roof water, a swale canal for collecting road water, and added additional structures to open areas to observe urbanization; scenario five (SN5) considered multiple GI implementations; and scenario six (SN6) considered full urbanization. The results indicate that greener infrastructure implementation provides benefits in reducing both the runoff coefficient and the peak flowrate, and the flood inundation area and number of structures affected by flood risk were decreased. The integrated evaluation system, which consisted of the geographic information system and the assessment of the 1D HEC-RAS hydrologic model, was applied to evaluate the GI usage and flood mitigation.

Patient mobility can be defined as a patient's movement or utilization of a health care service located in a place or region other than the patient's place of residence. Mobility provides freedom to patients to obtain health care from providers across regions and even countries. It is essential to monitor patient choices in order to maintain the quality standards and responsiveness of the health system, otherwise, the health system may suffer from geographic disparities in the accessibility to quality and responsive health care. In this article, we study patient mobility in a national health care system to identify medical regions, spatio-temporal and service characteristics of health care utilization, and demands for patient mobility. We conducted a systematic analysis of province-to-province patient mobility in Turkey from December 2009 to December 2013, which was derived from 1.2 billion health service records. We first used a flow-based regionalization method to discover functional medical regions from the patient mobility network. We compare the results of data-driven regions to designated regions of the government in order to identify the areas of mismatch between planned regional service delivery and the observed utilization in the form of patient flows. Second, we used feature selection, and multivariate flow clustering to identify spatio-temporal characteristics and health care needs of patients on the move. Medical regions we derived by analyzing the patient mobility data showed strong overlap with the designated regions of the Ministry of Health. We also identified a number of regions that the regional service utilization did not match the planned service delivery. Overall, our spatio-temporal and multivariate analysis of regional and long-distance patient flows revealed strong relationship with socio-demographic and cultural structure of the society and migration patterns. Also, patient flows exhibited seasonal patterns, and yearly trends which correlate with implemented policies throughout the period. We found that policies resulted in different outcomes across the country. We also identified characteristics of long-distance flows which could help inform policy-making by assessing the needs of patients in terms of medical specialization, service level and type. Our approach helped identify (1) the mismatch between regional policy and practice in health care utilization (2) spatial, temporal, health service level characteristics and medical specialties that patients seek out by traveling longer distances. Our findings can help identify the imbalance between supply and demand, changes in mobility behaviors, and inform policy-making with insights.

Geospatial data and related technologies have become an increasingly important aspect of data analysis processes, with their prominent role in most of them. Serverless paradigm have become the most popular and frequently used technology within cloud computing. This paper reviews the serverless paradigm and examines how it could be leveraged for geospatial data processes by using open standards in the geospatial community. We propose a system design and architecture to handle complex geospatial data processing jobs with minimum human intervention and resource consumption using serverless technologies. In order to define and execute workflows in the system, we also propose new models for both workflow and task definitions models. Moreover, the proposed system has new Open Geospatial Consortium (OGC) Application Programming Interface (API) Processes specification-based web services to provide interoperability with other geospatial applications with the anticipation that it will be more commonly used in the future. We implemented the proposed system on one of the public cloud providers as a proof of concept and evaluated it with sample geospatial workflows and cloud architecture best practices.

Analysis of patient mobility in a country not only gives an idea of how the health-care system works, but also can be a guideline to determine the quality of health care and health disparity among regions. Even though determination of patient movement is important, it is not often realized that patient mobility could have a unique pattern beyond health-related endowments (e.g., facilities, medical staff). This study therefore addresses the following research question: Is there a way to identify regions with similar patterns using spatio-temporal distribution of patient mobility? The aim of the paper is to answer this question and improve a classification method that is useful for populous countries like Turkey that have many administrative areas. The data used in the study consist of spatio-temporal information on patient mobility for the period between 2009 and 2013. Patient mobility patterns based on the number of patients attracted/escaping across 81 provinces of Turkey are illustrated graphically. The hierarchical clustering method is used to group provinces in terms of the mobility characteristics revealed by the patterns. Clustered groups of provinces are analyzed using non-parametric statistical tests to identify potential correlations between clustered groups and the selected basic health indicators. Ineffective health-care delivery in certain regions of Turkey was determined through identifying patient mobility patterns. High escape values obtained for a large number of provinces suggest poor health-care accessibility. On the other hand, over the period of time studied, visualization of temporal mobility revealed a considerable decrease in the escape ratio for inadequately equipped provinces. Among four of twelve clusters created using the hierarchical clustering method, which include 64 of 81 Turkish provinces, there was a statistically significant relationship between the patterns and the selected basic health indicators of the clusters. The remaining eight clusters included 17 provinces and showed anomalies. The most important contribution of this study is the development of a way to identify patient mobility patterns by analyzing patient movements across the clusters. These results are strong evidence that patient mobility patterns provide a useful tool for decisions concerning the distribution of health-care services and the provision of health care equipment to the provinces.