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Value of incorporating geospatial information into the prediction of on-street parking occupancy - A case study

Geo-Spatial Information Science 2021年第3期24卷 438-457页

作者： Michael Balmer Robert Weibel Haosheng Huang Department of Geography University of ZurichZurichSwitzerland Department of Geography Ghent UniversityGhentBelgium

In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many external data sources have been considered in the prediction models,the underlying geographic context has mostly been ignored.Thus,in order to study the contribution of geospatial information to parking occupancy prediction models,road network centrality,land use,and Point of Interest(POI)data were incorporated in Random Forest(RF)and Artificial Neural Network(ANN,specifically Feedforward Neural Network FFNN)prediction models in this work.Model performances were compared to a baseline,which only considers historical and temporal input data.Moreover,the influence of the amount of training data,the prediction horizon,and the spatial variation of the prediction were explored.The results show that the inclusion of geospatial information led to a performance improvement of up to 25%compared to the baseline.Besides,as the prediction horizon expanded,predictions became less reliable,while the relevance of geospatial data increased.In general,land use and POI data proved to be more beneficial than road network centrality.The amount of training data did not have a significant influence on the performance of the RF model.The ANN model,conversely,achieved optimal results on a training input of 5 days.Likely attributable to varying occupancy patterns,prediction performance disparities could be identified for different parking districts and street segments.Generally,the RF model outperformed the ANN model on all predictions.

关键词： Parking occupancy prediction on-street parking geospatial information machine learning

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Analysis of Spatial Autocorrelation Patterns of Heavy and Super-Heavy Rainfall in Iran

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Advances in Atmospheric Sciences 2017年第9期34卷 1069-1081页

作者： Iman ROUSTA Mehdi DOOSTKAMIAN Esmaeil HAGHIGHI Hamid Reza GHAFARIAN MALAMIRI Parvane YARAHMADI Department of Geography Yazd University Department of Geography Zanjan University Department of Geography Tabriz University Department of Geography Khwarizmi University

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran＇s heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord Gi statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.

关键词： Iran heavy rainfall super-heavy rainfall spatial autocorrelation Gi index

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Spatially explicit estimation of soil organic carbon stock of an estuarine mangrove wetland of eastern India using elemental analysis and very-fine resolution satellite data

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Ecological Processes 2022年第1期11卷 411-426页

作者： Debajit Datta Madhumita Bairagi Mansa Dey Argha Pratim Pal Jibananda Gayen Department of Geography Jadavpur UniversityKolkata 700032India Department of Geography Sree Chaitanya CollegeHabra 743268India

Background:This study estimated the total soil organic C(SOC)stock of the wetland influence zone of Bichitrapur mangroves in eastern India in a spatially explicit manner.Both spatial and vertical distribution of SOC densities with respect to land use/land cover(LULC)pattern were assessed.Subsequently,some site-specific management strate-gies were forwarded towards enhancement of C sequestration potential.Methods:The changing patterns of LULC within the wetland influence zone of the site were analyzed using Landsat TM(30 m)and Pleiades-1A(2 m)imageries from 1988 to 2018.Point-specific SOC measurement was done using samples taken from four core-depth intervals(viz.D1:0–20 cm,D2:20–40 cm,D3:40–70 cm,D4:70–100 cm)at 89 locations belonging to different LULC categories.Spatial interpolation was applied on this point-based data to produce SOC density and stock models as a whole and at all core-depths.Relationships between SOC density,core-depth and present LULC were evaluated through multivariate statistical analyses.Results:The LULC transformations during last three decades suggested the gradual growth of mangrove plantations as well as agricultural and aquacultural activities.Most amount of SOC was concentrated in D1(37.17%)followed by D3(26.51%),while D4 had the lowest(10.87%).The highest mean SOC density was observed in the dense mangrove patches(248.92 Mg ha^(−1))and the lowest mean was in the Casuarina plantations(2.78 Mg ha^(−1)).Here,Spline method emerged as the best-fit interpolation technique to model SOC data(R^(2)=0.74)and estimated total SOC stock of the entire wetland influence zone as 169,569.40 Mg and the grand mean as 125.56 Mg ha^(−1).Overall,LULC was inferred as a major determinant of SOC dynamics with a statistically significant effect(p<0.001),whereas no such inference could be drawn for soil core-depth.Conclusions:The C sequestration potential of sites such as the present one could be increased with appropriate zone-wise plantation strategies,restriction on the land conversion to aquaculture and pr

关键词： Blue C density Core-depth Coastal wetland Dry combustion Land use conversion Wetland influence zone

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Evaluation of hydro-chemistry in a phreatic aquifer in the Vindhyan Region, India, using entropy weighted approach and geochemical modelling

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Acta Geochimica 2023年第4期42卷 648-672页

作者： Ashutosh Mishra Aman Rai Prabuddh Kumar Mishra Suresh Chand Rai Department of Geography Delhi School of EconomicsUniversity of DelhiDelhi 110027India Department of Geography Shivaji CollegeUniversity of DelhiDelhi 110027India

Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research was intended to investigate the hydrogeochemical attributes and mechanisms regulating the chemistry of groundwater as well as to assess spatial variation in groundwater quality in Satna district,India.To accomplish this,the groundwater data comprising 13 physio-chemical parameters from thirty-eight phreatic aquifer locations were analysed for May 2020 by combining entropy-weighted water quality index(EWQI),multivariate statistics,geochemical modelling,and geographical information system.The findings revealed that the groundwater is fresh and slightly alkaline.Hardness was a significant concern as 57.89% of samples were beyond the permissible limit of the World Health Organisation.The dominance of ions were in the order of Ca^(2+)> Na^(+)> Mg^(2+)> K^(+) and HCO_(3)^(-)> SO_(4)^(2-)> Cl^-> NO_(3)^(-)> F^(-).Higher concentration of these ions is mainly concentrated in the northeast and eastern regions.Pearson correlation analysis and principal component analysis(PCA) demonstrated that both natural and human factors regulate groundwater chemistry in the region.The analysis of Q-mode agglomerative hierarchical clustering highlighted three significant water clusters.Ca-HCO_3 was the most prevalent hydro-chemical facies in all three clusters.Geochemical modelling through various conventional plots indicated that groundwater chemistry in the research region is influenced by the dissolution of calcite/dolomite,reverse ion exchange,and by silicate and halite weathering.EWQI data of the study area disclosed that 73.69% of the samples were appropriate for drinking.Due to high salinity,Magnesium(Mg^(2+)),Nitrate(NO_(3)^(-)),and Bicarbonate(HCO_(3)^(-)) concentrations,the north-central and north-eastern regions are particularly susceptible.The findings of the study may be accomplished by policymakers and groundwat

关键词： Groundwater quality assessment EWQI Multivariate statistical analysis Geochemical modelling Hydrogeochemical processes Saturation index

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Comparing the terrain reversal effect in satellite images and in shaded relief maps:an examination of the effects of color and texture on 3D shape perception from shading

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International Journal of Digital Earth 2019年第4期12卷 442-459页

作者： Arzu Çöltekin Julien Biland Department of Geography University of ZurichZurichSwitzerland Department of Geography University of BernBernSwitzerland

Terrain reversal effect(TRE)causes reversed 3D shape perception in satellite images and shaded relief maps(SRMs),and introduces difficulties in identifying landforms such as valleys and ridges.With this paper,in a controlled laboratory experiment,we compare how well 27 participants could identify valleys and ridges over 33 locations using SRMs,color satellite images and grayscale satellite images.The main depth cue is shadow both in vertical-view images and SRMs.However,the presence of texture and color in images also affect 3D shape perception.All our participants experience the illusion strongly:with the SRMs,it is very severe(2%accuracy),with grayscale images low but considerably better than SRMs(17.6%accuracy),and slightly worse with color imagery(15.3%accuracy).These differences between SRMs and imagery suggest that the participants who are able to bypass the illusion consciously or subconsciously interpret the photographic information.We support this observation further with a cue-strength analysis.Furthermore,we provide exploratory analyses of the effects of expertise,global convexity bias,and bistable perception.Our original empirical observations serve towards a better understanding of this visual illusion,and contribute towards nuanced and appropriate solutions to correcting for TRE differently for satellite images and SRMs.

关键词： Terrain reversal relief inversion false topographic perception phenomenon photo interpretation visual illusion user study bistable perception global convexity bias

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Local tips, global impact: community-driven measures as avenues of promoting inclusion in the control of neglected tropical diseases: a case study in Kenya

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Infectious Diseases of Poverty 2022年第4期11卷 81-90页

作者： Elizabeth A.Ochola Diana M.S.Karanja Susan J.Elliott Department of Geography and Environmental Management University ofWaterlooWaterlooONCanada

Background:Neglected tropical diseases(NTDs)affect poor populations with little or no’’political voice’to influence control activities.While most NTDs have interventions that work,the biggest challenge remains in delivering targeted interventions to affected populations residing in areas experiencing weak health systems.Despite the upward development trends in most countries of sub-Saharan Africa(SSA),the healthcare worker to population ratio remains exceptionally low,with some areas not served at all;thus,there is a need to involve other personnel for school and community-based healthcare approaches.Nonetheless,the current community-based programs suffer from inconsistent community participation due to a lack of coordinated response,and an expanded intervention agenda that lacks context-specific solutions applicable to rural,urban,and marginalized areas.Methods:This research investigated the capacity of local communities to address the burden of NTDs.Informed by the social theory of human capability,the research collected primary qualitative data by conducting key informant interviews and focus group discussions of people infected or affected by NTDs.The interview data were collected and transcribed verbatim for thematic analysis using Nvivo version 12.Results:Our findings reveal,first,a need for intersectoral collaboration between governments and affected populations for inclusive and sustainable NTD solutions.Second,a’’bottom-up’approach that enhances capacity building,sensitization,and behaviour change for improved uptake of NTD interventions.Third,the enforcement of Public Health Legislative Acts that mandates the reporting and treatment of NTDs such as leprosy.Fourth,the establishment of support groups and counseling services to assist persons suffering from debilitating and permanent effects of NTDs.Conclusions:Our research demonstrates the importance of human agency in encouraging new forms of participation leading to the co-production of inclusive and sustainable solutions against NTDs.

关键词： Neglected tropical diseases Control Community-driven measure Local solution Global impact Inclusion Kenya

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Influence of changes in extreme daily rainfall distribution on the stability of residual soil slopes

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Big Earth Data 2023年第1期7卷 101-125页

作者： Thapthai Chaithong Department of Geography Faculty of Social SciencesKasetsart UniversityBangkokThailand

Many landslides triggered by intense rainfall have occurred in moun-tainous areas in Thailand,causing major economic losses and infra-structure damage.Extreme daily rainfall is a significant trigger for hillslope instability.Increases in extreme daily rainfall intensity due to climate change may be one of the key factors responsible for the increased landslides.Thus,in this context,changes in the intensity of extreme daily rainfall in Chiang Mai Province in North Thailand and their effects on hillslope stability are analyzed.Extreme rainfall is modeled using a generalized extreme value distribution and esti-mated for various return periods.A numerical analysis of seepage and an infinite slope stability model are combined to understand the hillslope response under extreme rainfall conditions.The analysis period is divided into two periods of 34 years:1952 to 1985 and 1986 to 2019.According to the analysis results,the distribution of extreme daily rainfall changes in terms of location.The average annual daily maximum rainfall increased by approximately 11.13%.The maximum decrease in the safety factor is approximately 4.5%;therefore,these changes in extreme daily rainfall should be consid-ered in future landslide prevention policies.

关键词： Extreme rainfall generalized extreme value distribution slope stability landslide pore water pressure climate change residual soil

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Examining El Nio in the Holocene: implications and challenges

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National Science Review 2018年第6期5卷 807-809页

作者： Zhengyao Lu Zhengyu Liu department of Physical geography and Ecosystem Science Lund University Department of Geography Ohio State University

The El Nio–Southern Oscillation(ENSO) is characterized by year-to-year anomalous surface climate variability over the tropical Pacific. Through atmospheric teleconnections, ENSO can bring droughts and floods worldw... 详细信息

关键词： Examining El Ni implications and challenges o in the Holocene El

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Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

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Geo-Spatial Information Science 2019年第1期22卷 23-34页

作者： Ali Almagbile Department of Geography Yarmouk UniversityIrbidJordan

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.***.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.

关键词： Unmanned Aerial Vehicle(UAV) crowd density corner detection Feature from Accelerated Segment Test(FAST)algorithm clustering analysis

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Exploring the factors affecting urban ecological risk:A case from an Indian mega metropolitan region

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Geoscience Frontiers 2023年第1期14卷 286-297页

作者： Manob Das Arijit Das Ashis Mandal Department of Geography University of Gour BangaMaldaWest BengalIndia

Assessment of ecological risk(ER)is a key approach to adapting and mitigating ecological deterioration in cities of developing countries.In developing countries,the ecological landscapes such as vegetation cover,water bodies,and wetlands are highly vulnerable due to rapid urban expansion.Therefore,urban ER(UER)assessment and its drivers are crucial to guide ecological protection as well as restoration.This study aims to explore the spatiotemporal pattern of UER and the impact of urban spatial form on UER in the Kolkata Megacity Region(KMR),India.This study developed a UER index and used spatial regression models across the urban centres.The ER has been assessed at city scale as well as grid-scale(2 km x 2 km and 5 km x 5 km)from 2000 to 2020.The results showed that ER has substantially increased over the last 20 years.The urban centres with very high and high ER substantially increased,i.***.from 21.95%in 2000 to 31.70%in 2020.Kolkata and its surrounding urban centres were mostly characterized by very high and high ER.ER was influenced by spatial variables(such as land use and landscapes pattern).However,remote sensing parameters were weakly related to ER.The spatial lag model(SLM)(R^(2)=0.8686)was found to be better fit model than spatial error model(SEM)(R^(2)=0.8661)and ordinary linear regression model(OLS)(R^(2)=0.8641).Thus,the findings of the study can improve research and a comprehensive framework for urban ecological resources and provide a scientific basis for urban ecosystem planning and restoration.In addition to this,it will guarantee the sustainable utilization of urban ecosystems.

关键词： Urban ecological risk Kolkata metropolitan area Ecosystem services Ecological protection Urban form

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