Human Geographies最新文献

{"title":"Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information","authors":"Fernando Orduna-Cabrera, Marcial Sandoval-Gastelum, I. McCallum, L. See, S. Fritz, Santosh Karanam, T. Sturn, Valeria Javalera-Rincon, F. F. González-Navarro","doi":"10.3390/geographies3030029","DOIUrl":"https://doi.org/10.3390/geographies3030029","url":null,"abstract":"The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80109769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Spatial Autocorrelation: An Everyday Metaphor and Additional New Interpretations","authors":"D. Griffith","doi":"10.3390/geographies3030028","DOIUrl":"https://doi.org/10.3390/geographies3030028","url":null,"abstract":"An enumeration of spatial autocorrelation’s (SA’s) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student’s or spatial scientist’s personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture’s existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76207679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a Permafrost Vulnerability Index for Critical Infrastructure, Community Resilience and National Security","authors":"L. Alessa, James Valentine, Sean K Moon, Christopher McComb, Sierra Hicks, V. Romanovsky, Ming Xiao, A. Kliskey","doi":"10.3390/geographies3030027","DOIUrl":"https://doi.org/10.3390/geographies3030027","url":null,"abstract":"There has been a growth in the number of composite indicator tools used to assess community risk, vulnerability, and resilience, to assist study and policy planning. However, existing research shows that these composite indicators vary extensively in method, selected variables, aggregation methods, and sample size. The result is a plethora of qualitative and quantitative composite indices to choose from. Despite each providing valuable location-based information about specific communities and their qualities, the results of studies, each using disparate methods, cannot easily be integrated for use in decision making, given the different index attributes and study locations. Like many regions in the world, the Arctic is experiencing increased variability in temperatures as a direct consequence of a changing planetary climate. Cascading effects of changes in permafrost are poorly characterized, thus limiting response at multiple scales. We offer that by considering the spatial interaction between the effects of permafrost, infrastructure, and diverse patterns of community characteristics, existing research using different composite indices and frameworks can be augmented. We used a system-science and place-based knowledge approach that accounts for sub-system and cascade impacts through a proximity model of spatial interaction. An estimated ‘permafrost vulnerability surface’ was calculated across Alaska using two existing indices: relevant infrastructure and permafrost extent. The value of this surface in 186 communities and 30 military facilities was extracted and ordered to match the numerical rankings of the Denali Commission in their assessment of permafrost threat, allowing accurate comparison between the permafrost threat ranks and the PVI rankings. The methods behind the PVI provide a tool that can incorporate multiple risk, resilience, and vulnerability indices to aid adaptation planning, especially where large-scale studies with good geographic sample distribution using the same criteria and methods do not exist.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81698469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Empirical ETo Relationships with ERA5-Land and In Situ Data in Greece","authors":"Nikolaos Gourgouletis, Marianna Gkavrou, E. Baltas","doi":"10.3390/geographies3030026","DOIUrl":"https://doi.org/10.3390/geographies3030026","url":null,"abstract":"Reference evapotranspiration (ETo) estimation is essential for water resources management. The present research compares four different ETo estimators based on reanalysis data (ERA5-Land) and in situ observations from three different cultivation sites in Greece. ETo based on FAO56-Penman–Monteith (FAO-PM) is compared to ETo calculated from the empirical methods of Copais, Valiantzas and Hargreaves-Samani using both reanalysis and in situ data. The daily and monthly biases of each method are calculated against the FAO56-PM method. ERA5-Land data are also compared to ground-truth observations. Additionally, a sensitivity analysis is conducted on each site for different cultivation periods. The present research finds that the use of ERA5-Land data underestimates ground-truth-based ETo by 35%, approximately, when using the FAO56-PM method. Additionally, the use of other methodologies also shows underestimation of ETo when calculated with ERA5-Land data. On the contrary, the use of the Valiantzas and Copais methodologies with in situ observations shows overestimation of ETo when compared to FAO56-PM, in the ranges of 32–62% and 24–56%, respectively. The sensitivity analysis concludes that solar radiation and relative humidity are the most sensitive variables of the Copais and Valiantzas methodologies. Overall, the Hargreaves-Samani methodology was found to be the most efficient tool for ETo estimation. Finally, the evaluation of the ERA5-Land data showed that only air temperature inputs can be utilized with high levels of confidence.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81675761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Who Will Be Affected by Shifting Climate Zones","authors":"A. Malone","doi":"10.3390/geographies3030025","DOIUrl":"https://doi.org/10.3390/geographies3030025","url":null,"abstract":"Climate change is altering the conditions to which communities have adapted. The Köppen–Geiger classification system can provide a compact metric to identify regions with notable changes in climatic conditions. Shifting Köppen–Geiger climate zones will be especially impactful in regions with large populations. This study uses high-resolution datasets on Köppen–Geiger climate zones and populations to quantify the number of people affected by shifting climate zones (i.e., population exposure to shifting climate zones). By the end of this century, 9–15% of the Earth’s land surface is projected to shift its climate zone. These shifts could affect 1.3–1.6 billion people (14–21% of the global population). Many of the affected people live in areas that were classified as temperate in the historical period. These areas are projected to be classified as tropical or arid in the future. This study presents a new metric for exposure to climate change: the number of people living in areas whose climate zone classification is projected to shift. It also identifies populations that may face climatic conditions in the future that deviate from those to which they have adapted.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79854746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bird’s-Eye View of Colonias Hosting Forgotten Americans and Their Community Resilience in the Rio Grande Valley","authors":"Dean Kyne","doi":"10.3390/geographies3030024","DOIUrl":"https://doi.org/10.3390/geographies3030024","url":null,"abstract":"Colonia communities, which host forgotten Americans, lack essential services such as portable water, adequate wastewater and solid waste disposal, adequate drainage, and adequate paved roads. The aim of this study is to investigate five key aspects of the colonias in the Rio Grande Valley (RGV), which include the total count of colonias in the valley, their susceptibility to public health hazards, flooding occurrences, the transformations that have occurred over the past two decades, and community resilience. This research utilizes two datasets, namely the Colonia Database from the Texas Secretary of State and the community resiliency estimates from the Census Bureau. Geographical information systems (GIS) methods are employed to analyze the spatial and temporal distribution of colonia communities. The principal results reveal that colonia communities host 14% of the RGV’s total 1.37 million population. About half of the total colonia population resides in Hidalgo County, followed by Starr, Cameron, and Willacy counties. About 87% of the total colonia communities exist in census tracts characterized by low or very low community resiliency. Furthermore, 26% of the total colonia communities experiencing flooding after rainfall are in tracts with low or very low community resiliency. This study provides the major conclusion that while there have been slight improvements in the colonias’ susceptibility to public health risks within the past two decades, there still remains significant developmental work. Without tackling these challenges, achieving meaningful progress in community resilience becomes a daunting task. Applying an environmental justice lens to the issues faced by colonia communities helps shed light on the systemic inequalities and injustices they experience.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78755619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OpenDroneMap: Multi-Platform Performance Analysis","authors":"Augustine-Moses Gaavwase Gbagir, Kylli Ek, A. Colpaert","doi":"10.3390/geographies3030023","DOIUrl":"https://doi.org/10.3390/geographies3030023","url":null,"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. 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.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87033552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Modern Nile Delta Continental Shelf, with an Evolving Record of Relict Deposits Displaced and Altered by Sediment Dynamics","authors":"O. Frihy, J. Stanley","doi":"10.3390/geographies3030022","DOIUrl":"https://doi.org/10.3390/geographies3030022","url":null,"abstract":"The most extensive coverage of surficial sediment samples collected to date on Egypt’s Nile Delta coast and shelf is needed to better define sediment dispersal patterns across this setting’s rapidly eroding margin. Changes in time are now induced by River Nile sediment cutoff by dams, sea level rise, marked shelf subsidence, and regional climate changes, which have altered the amounts and components of sediments; these require replacement, along with the implementation of more effective coastal protection measures. Multiple computer-generated offshore maps depict the distributions and proportions of sand, silt, and mud; the mean grain size and standard deviation (sorting); heavy mineral concentrations; and carbonate content. Heavy mineral lobes at the coast and offshore identify former Nile branch sites. Channel lobes extending seaward resulted from their progradational phase and from the delta’s altered sedimentation from the early to late Holocene. The progressive deposition and erosion of these fossil fluvial lobes, and of two active Nile channels, selectively removed their quartz and less dense minerals, thus concentrating heavy minerals on the coast and inner shelf. The prolonged dispersal of original sediment effluence from relict and recent Nile tributaries induced variable depositional patterns on the present shelf. These coastal depocenters, along with extensive sand, silt, and mud from shelf sediments, were reworked further seaward and dispersed by bottom currents, thus masking most previous onshore-to-offshore transport patterns. The major surficial features document long-term responses to the diverse dispersal that influenced the shoreline to the outer shelf deposits from the Pleistocene to the present.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90461265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geovisualization of Historical Geospatial Data: A Web Mapping Application for the 19th-Century Kaupert’s Maps of Attica","authors":"G. Lampropoulos, G. Panagiotopoulos, Christina Giannakoula, Alexandros Kokkalas","doi":"10.3390/geographies3020021","DOIUrl":"https://doi.org/10.3390/geographies3020021","url":null,"abstract":"This paper presents the development procedure and significance of a web mapping application designed for disseminating, exploring, and analyzing Kaupert’s 19th-century Maps of Attica, Greece. The application facilitates historical and geographical study by providing access to high-resolution map images and overlaying multiple vector layers of geospatial data. The paper outlines the methods used to create the application, which includes the process of interpreting, digitizing, and organizing the original mapped data, georeferencing the historical cartographic sheets, and developing the web-based mapping application. The results of this work include a comprehensive and interactive digital reference tool for studying the ancient topography of Attica, as well as a framework for future research. Overall, this work highlights the potential of digital technologies to transform the way we approach and study historical maps and other cultural artifacts.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82992618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area.","authors":"Nathaniel R Geyer, Eugene J Lengerich","doi":"10.3390/geographies3020015","DOIUrl":"10.3390/geographies3020015","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73535519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}