Movement and Protection for Random Shape Rockfalls in Steeply Dipping Coal Seams

In view of the randomness of rockfalls shape and irregularity of the bottom floor of working face in steeply dipping coal seams (SDCS), it is difficult to accurately simulate rockfall movement, and it is consequently unable to effectively protect against multirockfalls. Therefore, a method for generating random shape rockfalls based on ellipsoid equation is proposed, and a 3D grid model of real bottom floor of working face is established based on the geographic information system data. In order to verify the accuracy and feasibility of the method and 3D model, the trajectory simulated by Rockyfor3D software is compared, and the proposed method and 3D model prove to be effective in simulating rockfall movement more accurately. Then the proposed method and 3D grid model are applied to solve the problem of multirockfalls protection in numerical simulation, and the main factors affecting the structural stress response of protective netting are analyzed by taking three incident modes of parallel heights, ladder parallel, and the same trajectory. In the simulation, it is found out that the trajectory of irregular rockfalls is greatly affected by the shape of rockfall and working face floor; during the process of multiple rockfalls colliding with the protective netting, the peak stress on the protective netting is inversely proportional to both the time interval between each rockfall and the distance between each rockfall. The findings presented in this research contribute to rockfall prediction and protection against rockfall hazards.

Automatic Identification of Roadway Horizontal Alignment Information Using Geographic Information System Data: CurvS Tool

This paper introduces CurvS, a web-based tool for researchers and analysts that automatically extracts, visualizes, and analyzes roadway horizontal alignment information using readily available geographic information system roadway centerline data. The functionalities of CurvS are presented along with a brief background on its methodology. The validation of its estimation results are presented using actual horizontal alignment data from two different roadway types: Route 83, a two-lane two-way rural roadway in New Jersey and I-80, a freeway segment in Nevada. Different metrics are used for validation. These are identification rates of curved and tangent sections, overlap ratio of curved and tangent sections between estimated and actual horizontal alignment data, and percent fit of curve radii. The validation results show that CurvS is able to identify all the curves on these two roadways, and the estimated section lengths are significantly close to the actual alignment data, especially for the I-80 freeway segment, where 90% of curved length and 94% of tangent section length are correctly matched. Even when curves have small central angles, such as the ones in Route 83, CurvS’s estimations covers 71% of curved length and 96% of tangent section length.

Feature Selection for Enhancing Purpose Imputation using Global Positioning System Data without Geographic Information System Data

This paper presents a method for enhancing purpose imputation from global positioning system data without using geographic information system data via relevant feature selection from six groups: (1) activity time; (2) user characteristics; (3) predicted travel modes; (4) actual travel modes; (5) estimated home location; and (6) estimated location of the most frequently visited non-home place (MFVP). Two datasets were collected in 2019 using TRavelVU, a smartphone application. The first one (the Hanoi dataset) comprised 652 days’ worth of data collected from 63 users in Hanoi, Vietnam, whereas the second one (the Donate dataset) comprised 932 days’ worth of information collected from 65 individuals in Denmark, Sweden, and Norway. The hyperparameters of the random forest models were tuned carefully in accordance with selected features, thereby facilitating a thorough evaluation of the improvement in prediction models. The findings of this study revealed that the addition of either actual or predicted modes resulted in improved imputation performance, albeit the former exhibited a stronger positive effect. This demonstrated the potential benefits of integrating mode detection and purpose identification into a continuous process. The newly adopted MFVP feature contributed to enhanced prediction results (around 2%). The proposed purpose-imputation models, which benefited from all features, demonstrated accuracies of the order of 75% and 85% for the Hanoi and Donate datasets, respectively. The imputation of home and work/education activities demonstrated high success, whereas reasonable prediction results with nearly all F-score levels ranging between 50% and 83% were observed for pick-up/drop-off, shopping/eating, visit/leisure, and business activities.

Red Chalk Palimpsest: The Logic of Somba Landscape

This article presents the results of a study on the traditional settlement patterns of the Somba people, living in the department of Atacora, north-western Benin. Adopting a methodology based on both a generative approach and André Corboz’s (1983) territory–palimpsest analogy, the study specifically questions the ‘dispersed’ character of the Somba habitat. Built upon two hypotheses, according to which Tatas Somba settle approximately to pre-existing Tatas and near to watercourses, this study seeks to understand the reasons and conditions of this dispersal throughout history. By cross-checking on-site inventory and geographic information system data allowing to analyse the distances between Tatas, archaeological sites and nearby watercourses, and thus revealing the permanent, the persistent, and the disappeared landscape elements, this article aims to prove that the settlement of the Tatas Somba is not determined by geometrical compositions, landmarks, or infrastructures, but rather by a combination of social, agricultural, environmental, and subsistence factors.

Developing Plot Level Database on Land Accretion: A Case Study on Sandwip Island of Bangladesh

Erosion and accretion are continuously changing the shape, size and configuration of Sandwip Island of Bangladesh. Plot level database are very significant in order to document these changes from micro levels. For this purpose, an attempt has been made to develop plot level database about land accretion of the Island since 1913 through overlapping Mauza maps using GIS (Geographic Information System) data. Here a case study named Thak Kuchiamora mauza, experiencing alluvion located in the eastern side of the Sandwip Island has been presented from micro level. Both primary and secondary data have been used for this study. CS (Cadastral Survey) and RS (Revisional Settlement) mauza maps available from DLRS (Directorate of land records and Surveys) office has been collected and overlaid on satellite images (2016) so that the accretion context and circumstances can be detected. Plot level information has been extracted from collected mauza maps and integrating with present land cover features changing scenario has been analysed. During cadastral survey 172 plots have been recorded and in the Revisional settlement operation it increased into 502 plots due to land fragmentation. From 1970 to 2000 period the area accreted about 220 m from southwest to northeast direction. Analyzing accreted database over different period it has been concluded that from Revisional settlement operation (1970) to 2016 it has been recorded that the mauza has increased about 1185 m from west to east. The newly formed landmass of the area hasn’t stable enough for permanent land use yet.

Going Spatial: Applying Egohoods to Fear of Crime Research

A central theme in criminology is how fear of crime is influenced by the residential context. Most researchers rely on administrative neighbourhoods to define context. These administrative units do not necessarily align with how inhabitants experience their local surroundings. The present study combines administrative neighbourhoods with a more innovative way to measure context. Using geocoded survey data (N = 14.620) in combination with detailed geographic information system data, we construct egohoods with different radii (ranging from 50 to 750 m). We find that crime, ethnic diversity, economic status, disorder and facilities all have an effect on feelings of unsafety. The contextual effects differ in size and are not detected in all spatial contexts, indicating that it matters how and to which scale data are aggregated.

Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.