Advanced Technologies in Spatial Data Collection and Analysis

A special issue of Geographies (ISSN 2673-7086).

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 16665

Special Issue Editors


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Guest Editor

Special Issue Information

Dear Colleagues,

Over the past few years, a rapid development of new hard and software technologies, algorithms, and programming libraries for the collection and analysis of geographic data has taken place to provide state-of-the art solutions to current societal questions. Examples for such technologies are wearable devices for the collection of physiological data related to human emotions, GIS cloud computing, the integration of global satellite navigation systems (GNSS) into mobile devices, application portals for the collection and sharing of volunteered geographic information, social networking platforms, environmental monitoring networks, the Internet of Things (IoT) for building smart cities and intelligent transportation systems, real-time tracking systems for public transit vehicles, ridesharing systems, and artificial intelligence and deep learning for applications such as disaster monitoring or refugee movement pattern extraction. This Special Issue calls for research contributions presenting novel techniques, applications, sensors, devices, and technologies for the collection of spatial or spatiotemporal data, and effective processing of such data (including big data), through the development or use of new algorithms, software packages, or high-performance computing infrastructures. This Special Issue also welcomes discussions and reviews of previously underexplored open spatial data sets that are of relevance to the larger geoscience community to address research questions in different scientific fields. We invite contributions from a variety of academic disciplines, including geodesy, geo-information science, computer science, cartography, geography, transportation, environmental science, and health. This Special Issue is expected to foster awareness of research directions in other fields, understanding of methods applied by other disciplines, and collaboration between these research communities.

Dr. Hartwig H. Hochmair
Dr. Gerhard Navratil
Prof. Dr. Haosheng Huang
Guest Editors

Manuscript Submission Information

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Keywords

  • geospatial open-source software packages
  • innovative data collection methods and devices
  • big data analysis
  • analysis of sensor and network data
  • text mining for geographic problems
  • geospatial artificial intelligence (GeoAI)
  • geovisual analytics and visual data mining
  • location-based questions
  • role of advanced geospatial technologies in today’s society

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Published Papers (8 papers)

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Editorial

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5 pages, 231 KiB  
Editorial
Perspectives on Advanced Technologies in Spatial Data Collection and Analysis
by Hartwig H. Hochmair, Gerhard Navratil and Haosheng Huang
Geographies 2023, 3(4), 709-713; https://doi.org/10.3390/geographies3040037 - 02 Nov 2023
Viewed by 806
Abstract
The motivation to organize this Special Issue originated from the observation of rapid changes taking place in the domain of geographical information science and systems over the past few decades [...] Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)

Research

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23 pages, 11233 KiB  
Article
Assessing Rainfall Variability in Jamaica Using CHIRPS: Techniques and Measures for Persistence, Long and Short-Term Trends
by Cheila Avalon Cullen and Rafea Al Suhili
Geographies 2023, 3(2), 375-397; https://doi.org/10.3390/geographies3020020 - 26 May 2023
Cited by 5 | Viewed by 1599
Abstract
Jamaica, as a Small Island Developing State (SIDS), is highly vulnerable to weather extremes. As precipitation persistence is a critical factor in determining the susceptibility of an area to risks, this work assesses the spatial and temporal variations of rainfall persistence in Jamaica [...] Read more.
Jamaica, as a Small Island Developing State (SIDS), is highly vulnerable to weather extremes. As precipitation persistence is a critical factor in determining the susceptibility of an area to risks, this work assesses the spatial and temporal variations of rainfall persistence in Jamaica from 1981 to 2020, using satellite-based information. The Hurst exponent (H) and the serial correlation coefficient (SCC) are used to evaluate the long-term persistence of precipitation and the Persistence Threshold (PT) concept is introduced to provide a description of rainfall characteristics over short periods, specifically, the number of consecutive days with precipitation above or below a set threshold value. The PT method is a novel concept that expands upon the Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD) methods that only consider a threshold of 1 mm. Results show notable temporal and spatial variations in persistence over the decades, with an overall increasing trend in high precipitation persistence and a decreasing trend in low precipitation persistence. Geographically, the northern mountainous area of Jamaica received the most persistent rainfall over the study period with an observed increase in extreme rainfall events. The excess rainfall of the 2001–2010 decade is remarkable in this study, coinciding with the global unprecedented climate extremes during this time. We conclude that the data used in this study is viable for understanding and modeling rainfall trends in SIDS like Jamaica, and the derived PT method is a useful tool for short-term rainfall trends, but it is just one step toward determining flood or drought risk. Further research will focus on developing drought and flood indices. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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11 pages, 2123 KiB  
Article
PCIer: Pavement Condition Evaluation Using Aerial Imagery and Deep Learning
by Sisi Han, In-Hun Chung, Yuhan Jiang and Benjamin Uwakweh
Geographies 2023, 3(1), 132-142; https://doi.org/10.3390/geographies3010008 - 01 Feb 2023
Cited by 3 | Viewed by 1654
Abstract
This paper aims to explore and evaluate aerial imagery and deep learning technology in pavement condition evaluation. A convolutional neural network (CNN) model, named PCIer, was designed to process aerial images and produce pavement condition index (PCI) estimations, which are classified into [...] Read more.
This paper aims to explore and evaluate aerial imagery and deep learning technology in pavement condition evaluation. A convolutional neural network (CNN) model, named PCIer, was designed to process aerial images and produce pavement condition index (PCI) estimations, which are classified into four scales of Good (PCI ≥ 70), Fair (50 ≤ PCI < 70), Poor (25 ≤ PCI < 50), and Very Poor (PCI < 25). In the experiment, the PCI datasets were retrieved from the published pavement condition report by the City of Sacramento, CA. Following the retrieved datasets, the authors also collected the corresponding aerial image datasets containing 100 images for each PCI grade from Google Earth. An 80% proportion of datasets were used for PCIer model training, and the remaining were used for testing. Comparisons showed using a 128-channel heatmap layer in the proposed PCIer model and saving the PCIer model with the best validation accuracy would yield the best performance, with a testing accuracy of 0.97, and a weighted average precision, recall, and F1-score of 0.98, 0.97, and 0.97, respectively. Moreover, future research recommendations are provided in the discussion for improving the effectiveness of pavement evaluation via aerial imagery and deep learning. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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20 pages, 6037 KiB  
Article
Comparison of Earthquake and Moisture Effects on Rockfall-Runouts Using 3D Models and Orthorectified Aerial Photos
by Mohammad Al-Shaar, Pierre-Charles Gérard, Ghaleb Faour, Walid Al-Shaar and Jocelyne Adjizian-Gérard
Geographies 2023, 3(1), 110-129; https://doi.org/10.3390/geographies3010006 - 16 Jan 2023
Cited by 1 | Viewed by 2000
Abstract
Rockfall hazard gains popularity nowadays among researchers in different scientific fields, decision-makers and urban planners. The assessment of rockfall hazard requires detection, mapping and estimating the maximum travel distance that rock boulders may reach, commonly known as “rockfall runout”. This latter can change [...] Read more.
Rockfall hazard gains popularity nowadays among researchers in different scientific fields, decision-makers and urban planners. The assessment of rockfall hazard requires detection, mapping and estimating the maximum travel distance that rock boulders may reach, commonly known as “rockfall runout”. This latter can change significantly under the effects of different triggering factors such as soil conditions, chemical, physical and geological rock properties. However, comparing and analyzing these different effects represents, to the best of our knowledge, one of the newest scientific challenges that need to be addressed. This paper presents a complete methodologic approach aiming to assess the rockfall hazard through runout estimation in three different conditions: (i) gravity, (ii) earthquakes, and (iii) the presence of moisture along the slope. The “Mtein” Village and its surrounding areas in the Mount Lebanon region were chosen as the study area because there have been numerous historic rockfalls and various-sized rocks, such as cobbles and boulders, scattered throughout the area. Thus, three-dimensional simulations were conducted using the Rockyfor3D software and aerial photos for the year 1999 to assess the rockfall runout, the energy curves, and the number of deposited rocks. The results reveal that earthquakes have the highest triggering effect on rockfall and that moisture has a damping effect on RFs by decreasing the kinetic energy. The study shows the importance of taking into consideration the influence of triggering factors as well as rock density on rockfall runout and hazard. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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14 pages, 4060 KiB  
Article
The Effect of Twitter App Policy Changes on the Sharing of Spatial Information through Twitter Users
by Jiping Cao, Hartwig H. Hochmair and Fisal Basheeh
Geographies 2022, 2(3), 549-562; https://doi.org/10.3390/geographies2030033 - 05 Sep 2022
Cited by 4 | Viewed by 2131
Abstract
Social media data have been widely used to gain insight into human mobility and activity patterns. Despite their abundance, social media data come with various data biases, such as user selection bias. In addition, a change in the Twitter app functionality may further [...] Read more.
Social media data have been widely used to gain insight into human mobility and activity patterns. Despite their abundance, social media data come with various data biases, such as user selection bias. In addition, a change in the Twitter app functionality may further affect the type of information shared through tweets and hence influence conclusions drawn from the analysis of such data. This study analyzes the effect of three Twitter app policy changes in 2015, 2017, and 2019 on the tweeting behavior of users, using part of London as the study area. The policy changes reviewed relate to a function allowing to attach exact coordinates to tweets by default (2015), the maximum allowable length of tweet posts (2017), and the limitation of sharing exact coordinates to the Twitter photo app (2019). The change in spatial aspects of users’ tweeting behavior caused by changes in user policy and Twitter app functionality, respectively, is quantified through measurement and comparison of six aspects of tweeting behavior between one month before and one month after the respective policy changes, which are: proportion of tweets with exact coordinates, tweet length, the number of placename mentions in tweet text and hashtags per tweet, the proportion of tweets with images among tweets with exact coordinates, and radius of gyration of tweeting locations. The results show, among others, that policy changes in 2015 and 2019 led users to post a smaller proportion of tweets with exact coordinates and that doubling the limit of allowable characters as part of the 2017 policy change increased the number of place names mentioned in tweets. The findings suggest that policy changes lead to a change in user contribution behavior and, in consequence, in the spatial information that can be extracted from tweets. The systematic change in user contribution behavior associated with policy changes should be specifically taken into consideration if jointly analyzing tweets from periods before and after such a policy change. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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16 pages, 785 KiB  
Article
The Choice of Actor Variables in Agent-Based Cellular Automata Modelling Using Survey Data
by Glen Searle, Siqin Wang, Michael Batty and Yan Liu
Geographies 2022, 2(1), 145-160; https://doi.org/10.3390/geographies2010010 - 19 Mar 2022
Cited by 1 | Viewed by 1816
Abstract
This paper considers whether existing approaches for quantifying variables in cellular automata (CA) modelling adequately incorporate all the relevant factors in typical actor decisions underpinning urban development. A survey of developers and planners is used to identify factors they incorporate to allow for [...] Read more.
This paper considers whether existing approaches for quantifying variables in cellular automata (CA) modelling adequately incorporate all the relevant factors in typical actor decisions underpinning urban development. A survey of developers and planners is used to identify factors they incorporate to allow for or proceed with development, using South East Queensland as a reference region. Three types of decision factors are identified and ranked in order of importance: those that are already modelled in CA applications; those that are not modelled but are quantifiable; and those that are not (easily) quantifiable because they are subjective in nature. Factors identified in the second category include development height/scale, open space supply, and existing infrastructure capacity. Factors identified in the third category include political intent, community opposition, and lifestyle quality. Drawing on our analysis of these factors we suggest how and to what extent survey data might be used to address the challenges of incorporating actor variables into the CA modelling of urban change. The paper represents the first attempt to review what decision factors should be included in CA modelling, and how this might be enabled. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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Other

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13 pages, 5387 KiB  
Technical Note
OpenDroneMap: Multi-Platform Performance Analysis
by Augustine-Moses Gaavwase Gbagir, Kylli Ek and Alfred Colpaert
Geographies 2023, 3(3), 446-458; https://doi.org/10.3390/geographies3030023 - 17 Jul 2023
Cited by 2 | Viewed by 2163
Abstract
This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. [...] Read more.
This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. drone image datasets from Namibia and northern Finland. For testing, we used the OpenDroneMap command line tool with default settings and the fast orthophoto option, which produced a good quality orthomosaic. We also used the “rerun-all option” to ensure that all jobs started from the same point. Our results show that ODM processing time is dependent upon the number of images, a high number of which can lead to high memory demands, with low memory leading to an excessively long processing time. Adding additional CPU cores is beneficial to ODM up to a certain limit. A 20-core machine seems optimal for a dataset of about 1000 images, although 10 cores will result only in slightly longer processing times. We did not find any indication of improvement when processing larger datasets using 40-core machines. For 1000 images, 64 GB memory seems to be sufficient, but for larger datasets of about 8000 images, higher memory of up to 256 GB is required for efficient processing. ODM can use GPU acceleration, at least in some processing stages, reducing processing time. In comparison to commercial software, ODM seems to be slower, but the created orthomosaics are of equal quality. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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17 pages, 3563 KiB  
Technical Note
LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area
by Nathaniel R. Geyer and Eugene J. Lengerich
Geographies 2023, 3(2), 286-302; https://doi.org/10.3390/geographies3020015 - 18 Apr 2023
Cited by 2 | Viewed by 1605
Abstract
In 2018, the Penn State Cancer Institute developed LionVu, a web mapping tool to educate and inform community health professionals about the cancer burden in Pennsylvania and its catchment area of 28 counties in central Pennsylvania. LionVu, redesigned in 2023, uses several open-source [...] Read more.
In 2018, the Penn State Cancer Institute developed LionVu, a web mapping tool to educate and inform community health professionals about the cancer burden in Pennsylvania and its catchment area of 28 counties in central Pennsylvania. LionVu, redesigned in 2023, uses several open-source JavaScript libraries (i.e., Leaflet, jQuery, Chroma, Geostats, DataTables, and ApexChart) to allow public health researchers the ability to map, download, and chart 21 publicly available datasets for clinical, educational, and epidemiological audiences. County and census tract data used in choropleth maps were all downloaded from the sources website and linked to Pennsylvania and catchment area county and census tract geographies, using a QGIS plugin and Leaflet JavaScript. Two LionVu demonstrations are presented, and 10 other public health related web-GIS applications are reviewed. LionVu fills a role in the public health community by allowing clinical, educational, and epidemiological audiences the ability to visualize and utilize health data at various levels of aggregation and geographical scales (i.e., county, or census tracts). Also, LionVu is a novel application that can translate and can be used, for mapping and graphing purposes. A dialog to demonstrate the potential value of web-based GIS to a wider audience, in the public health research community, is needed. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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