Web-based systems allow users to filter data and resources using river network structure on interactive map environments that use server side processing. With the increasing resolution of river networks, optimized methods for storage of river network representation in databases and efficient queries on the river network structure become critical. This paper presents a detailed study of widely used methods for representing generic networks in relational databases and benchmarking common queries on river network data using these methods. The analysis has been applied to a data set consisting of the river network of Iowa, with over 620,000 individual subwatersheds/nodes in the network. For typical river network queries (path to the outlet; contributing watersheds), two of considered data models (Adjacency List and Nested Set) guarantee response times below 1 s. This will allow users to carry out large-scale analysis and visualizations tasks on the web for hydrological data sets. A new data model, Stream Network, is proposed based on Path Enumeration, stores directly additional hydrological information, enabling, for example, retrieval of the main stream.Plain Language Summary Web-based systems allow users to filter data and resources using river network structure on interactive map environments that use server side processing. With the increasing resolution of river networks, optimized methods for storage of river network representation in databases and efficient queries on the river network structure become critical. This paper presents a detailed study of widely used methods for representing generic networks in relational databases and benchmarking common queries on river network data using these methods.
At the turn of February and March 2020, COVID-19 pandemic reached Europe. Many countries, including Poland imposed lockdown as a method of securing social distance between potentially infected. Stay-at-home orders and movement control within public space not only affected the touristm industry, but also the everyday life of the inhabitants. The hourly time-lapse from four HD webcams in Cracow (Poland) are used in this study to estimate how pedestrian activity changed during COVID-19 lockdown. The collected data covers the period from 9 June 2016 to 19 April 2020 and comes from various urban zones. One zone is tourist, one is residential and two are mixed. In the first stage of the analysis, a state-of-the-art machine learning algorithm (YOLOv3) is used to detect people. Additionally, a non-standard application of the YOLO method is proposed, oriented to the images from HD webcams. This approach (YOLOtiled) is less prone to pedestrian detection errors with the only drawback being the longer computation time. Splitting the HD image into smaller tiles increases the number of detected pedestrians by over 50%. In the second stage, the analysis of pedestrian activity before and during the COVID-19 lockdown is conducted for hourly, daily and weekly averages. Depending on the type of urban zone, the number of pedestrians decreased from 33% in residential zones to 85% in tourist zones located in the Old Town. The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN. The result of the research is a published database with the detected number of pedestrians from the four-year observation period for four locations in Cracow.
Widespread, human‐induced land cover (LC) transitions in areas surrounding national parks and nature reserves, accompanied by changing climate conditions, can trigger serious consequences for these natural environments and their ecosystems. The aim of the study was to assess the direction and magnitude of transitions in LC in the Polish Carpathians since 1990 for determining the current status of land degradation neutrality and future degradation risk as well as to analyze concomitant changes in climate conditions to enable sustainable land management. The study area encompasses six national parks with the United Nations Educational, Scientific, and Cultural Organization biosphere reserves, several dozen nature reserves, and landscape parks and is a key source of drinking water in Poland. We studied detailed changes in LC (13 classes). The Wilcoxon signed rank test indicated significant increases in built‐up areas (urban fabric and industrial/transport infrastructure), waters, dense forest cover, and herbaceous vegetation but a decrease in heterogeneous agricultural areas. On the basis of two new methods, we assessed the LC transitions for 20 mountain catchments and for the entire Polish Carpathians. Although we found a slight positive transition in LC for the entire Polish Carpathians, clearly negative transitions occurred in the five analyzed catchments, including those covering two national parks. These LC transitions are accompanied by changing climate conditions with significant increases in air temperature and precipitation. The ongoing intensive urbanization in the studied mountain region, which is accompanied by significant regional warming, implies the need to apply sustainable management practices that will avoid or reduce degradation in the catchments involved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.