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Mass Valuation of Unimproved Land Value Case Study: Nairobi County

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Journal of Geographic Information System 2023年第1期15卷 122-139页

作者： Edwin Kochulem Dennis Mwaniki Felix Mutua Department of Geomatic Engineering and geospatial Information System (GEGIS) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya Data and analytics Section UN-Habitat Nairobi Kenya

The purpose of this study is to investigate mass valuation of unimproved land value using machine learning techniques. The study was conducted in Nairobi County. It is one of the 47 Kenyan Counties under the 2010 constitution. A total of 1440 geocoded data points containing the market selling price of vacant land in Nairobi were web scraped from major property listing websites. These data points were adopted as dependent variables given as unit price of vacant land per square meter. The Covariates used in this study were categorized into Accessibility, Environmental, Physical and Socio-Economic Factors. Due to multi-collinearity problem present in the covariates, PLS and PCA methods were adopted to transform the observed features using a set of vectors. These methods resulted in an uncorrelated set of components that were used in training machine learning algorithms. The dependent variable and uncorrelated components derived feature reduction methods were used as training data for training different machine learning regression models namely;Random forest, support vector regression and extreme gradient boosting regression (XGboost regression). PLS performed better than PCA because the former maximizes the covariance between dependent and independent variables while the latter maximizes variance between the independent variables only and ignores the relationship between predictors and response. The first 9 components were identified as significant both by PLS and PCA methods. The spatial distribution of vacant land value within Nairobi County was consistent for all the three machine learning models. It was also noted that the land value pattern was higher in the central business district and the pattern spread northwards and westwards relative to the CBD. A relative low vacant land value pattern was observed on the eastern side of the county and also at the extreme periphery of Nairobi County boundary. From the accuracy metrics of R-squared and MAPE, Random Forest Regression model performed better than XGBoost and

关键词： Machine Learning Property Valuation GIS PLS PCA

Spatio-temporal patterns and changes in environmental attitudes and place attachment in Gauteng, South Africa

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Geo-Spatial Information Science 2021年第4期24卷 666-677页

作者： Simangele Dlamini Solomon G.Tesfamichael Gregory D.Breetzke Tholang Mokhele Department of Geography Environmental Management and Energy StudiesUniversity of JohannesburgJohannesburgSouth Africa geospatial analytics eResearch Knowledge CentreHuman Sciences Research CouncilPretoriaSouth Africa Department of Geography Geoinformatics and MeteorologyUniversity of PretoriaPretoriaSouth Africa

In this study,we employed a number of geospatial techniques to examine the spatiotemporal patterns and changes of environmental attitudes and place attachment values in the Gauteng province of South Africa.The data were obtained from the Gauteng City Region Observatory’s Quality of Life Survey collected at three separate points in time,namely 2013,2015,and 2017.Results indicated that wards(smallest administrative and analysis units)located on the urban periphery of Gauteng,which are generally less affluent,largely held more negative environmental attitudes and place attachment values during the three time periods.In contrast,centrally located wards,which are generally more affluent,expressed more positive environmental attitudes but less place attachment values,especially in 2017.The findings of this research not only highlight the complex spatio-temporal distribution of environmental attitudes and place attachment values throughout Gauteng but also empha-size the need for spatially targeted state interventions for future environmental planning within the province.

关键词： Spatio-temporal changes environmental attitudes place attachment Gauteng spatial patterns

An Adaptive and Image-guided Fusion for Stereo Satellite Image Derived Digital Surface Models

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Journal of Geodesy and Geoinformation Science 2022年第4期5卷 1-9页

作者： Hessah ALBANWAN Rongjun QIN Department of Civil Engineering Kuwait UniversityKuwait 72504Kuwait geospatial Data analytics Lab The Ohio State UniversityColumbus 43210USA Department of Civil Environmental and Geodetic EngineeringThe Ohio State UniversityColumbus 43210USA Department of Electrical and Computer Engineering The Ohio State UniversityColumbus 43210USA Translational Data analytics Institute The Ohio State UniversityColumbus 43210USA

The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete DSM.However,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs.Therefore,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points.The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.

关键词： Digital Surface Model(DSM) DSM Fusion adaptive fusion satellite stereo images

How does map use differ in virtual reality and desktop-based environments?

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International Journal of Digital Earth 2020年第12期13卷 1484-1503页

作者： Weihua Dong Tianyu Yang Hua Liao Liqiu Meng Research Center of geospatial Cognition and Visual analytics and Faculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China College of Resources and Environmental Sciences Human Normal UniversityChangshaPeople’s Republic of China Department of Civil Geo and Environmental EngineeringTechnical University of MunichMunichGermany

Maps based on virtual reality(VR)are evolving and are being increasingly used in the field of geography.However,the advantages of VR based on the map use processes of users over desktop-based environments(DEs)are not fully understood.In this study,an experiment was conducted in which 120 participants performed map use tasks using maps and globes in VR and DE.The participants’eye movements and questionnaires were collected to compare the map use performance differences.We analyzed the general metrics,information searching and processing metrics of participants(e.***.response time,RT;average fixation duration,AFD;average saccade duration,ASD;saccade frequency,SF,etc.)using maps and globes in different environments.We found that the participants using VR processed information more efficiently(AFDDE=233.34 ms,AFDVR=173.09 ms),and the participants using DE had both a significantly shorter response time(RT_(DE)=88.68 s,RT_(VR)=124.05 s)and a shorter visual search time(ASD_(DE)=60.78 ms,ASD_(VR)=112.13 ms;SF_(DE)=6.30,SF_(VR)=2.07).We also found similarities in accuracy,satisfaction and readability.These results are helpful for designing VR maps that can adapt to human cognition and reflect the advantages of VR.

关键词： Virtual reality desktop-based environment map use performance map globe

A volumetric change detection framework using UAV oblique photogrammetry–a case study of ultra-high-resolution monitoring of progressive building collapse

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International Journal of Digital Earth 2021年第11期14卷 1705-1720页

作者： Ningli Xu Debao Huang Shuang Song Xiao Ling Chris Strasbaugh Alper Yilmaz Halil Sezen Rongjun Qin geospatial Data analytics Lab The Ohio State UniversityColumbusOHUSA Photogrammetric Computer Vision Lab The Ohio State UniversityColumbusOHUSA Department of Civil Environmental and Geodetic EngineeringThe Ohio State UniversityColumbusOHUSA Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOHUSA Translational Data analytics Institute The Ohio State UniversityColumbusOHUSA Engineering Technology Services The Ohio State UniversityColumbusOHUSA

In this paper,we present a case study that performs an unmanned aerial vehicle(UAV)based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition.The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds,achieving an average distance of 12 cm and 16 cm for roof and façade respectively.We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation(free of ground control points),reconstruction,and a coarse-to-fine 3D density change analysis.This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively.Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92,with consistently high precision(0.92–0.99).Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.

关键词： 3D change detection multitemporal data registration oblique photogrammetry

Proliferation of Cartographic Education in the Age of Big Data

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Journal of Geodesy and Geoinformation Science 2022年第3期5卷 7-18页

作者： Liqiu MENG Chair of Cartography and Visual analytics Technical University of MunichMunich 80333Germany

Maps have long been a part of everyday life for the general public,and even more so in today’s knowledge society.No doubt,cartography as a profession of map design is assuming a more important role in the formation of intellectual skills in terms of spatial reasoning.Since its emergence as an academic discipline about 100 years ago,cartography has undergone many paradigm shifts.Its interaction with other disciplines has also constantly unfolded.These changes have left traces in cartographic education programs.In the age of big data,however,we are facing four fundamental challenges:(1) cartographic courses are being marginalized or even disappearing from degree programs in geospatial sciences;(2) the role of cartographers is increasingly eclipsed as a side effect of participatory cartography;(3) cartographers are blamed whenever something goes wrong with map use;and(4) professional map publishers can hardly compete with online mapping platforms dominated by Internet giants.Based on a contextual analysis of this seemingly gloomy situation,the paper reveals a number of proliferation points for the design of future cartographic curricula.First,cartography,once dedicated to supporting geospatial sciences,is thriving in the soil of data science,mapping not only the earth or other celestial bodies,but literally any kind of virtual space.Second,cartography has benefited from theoretical and technological advances in cognitive sciences,especially non-intrusive user studies,so that spatial cognition is becoming an integral component of cartographic education.Third,the role of scapegoat for wrongdoing of maps has accentuated cartographer’s overarching responsibility for quality and ethical issues in the geodata value chain.Finally,the diversification of the labor market requires new approaches to prepare future talents for a coopetition-oriented ecosystem in the marketplace.

关键词： transdisciplinary spatial cognition transparency inclusiveness online mapping platform

Species Composition and Gear Characteristics of the Macrobrachium Fishery of the Cross River Estuary,Nigeria

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Journal of Ocean University of China 2010年第1期9卷 71-75页

作者： Francis M.Nwosu Institute of Oceanography University of CalabarPMB 1115CalabarNigeria geospatial analytics Laboratory 103 PNM BuildingUniversity of South FloridaSt.Petersburg140 Seventh Ave South St.PetersburgFL 33701USA

The Cross River Estuary,Nigeria,is an important shrimping area for artisanal fishermen of the coastal communities.The multi-species Macrobrachium fishery is exploited with three main gears,namely beach seine,push net and trap.Studies on species composition of this fishery recorded thirteen shrimp species,one swimming crab (Callinectes amnicola) and two fish species (Eleotris sp.and Pellonula leonensis).The shrimp species identified included Macrobrachium macrobrachion (83.39% and 55.69% by number and weight,respectively),M.vollenhovenii (9.66% and 37.18%),M.equidens (3.8% and 2.87%),juveniles-sub-adults of Penaeus notialis (1.11% and 1.3%),M.dux,M.felicinum,Palaemonetes africanus,Palaemon maculatus,Palaemon elegans,Desmocaris sp.,Leander sp.,Nematopalaemon hastatus and Alpheus pontederiae.While the selectivity index for trap was 0.25,beach seine and push net had a lower index of 0.063.The results present the first comprehensive and representative report for the Estuary shrimp fishery and will assist in the management of the biodiversity of this ecosystem.

关键词：日本沼虾渔业物种齿轮组成南美白对虾生物多样性尼日利亚

Big data analytics: six techniques

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Geo-Spatial Information Science 2016年第2期19卷中插3-中插3,119-128页

作者： Hong Shu The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (LIESMARS)Wuhan UniversityWuhanChina Collaborative Innovation Center of geospatial Technology Wuhan UniversityWuhanChina

Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data.The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.

关键词： Big data ensemble analysis association analysis high-dimensional analysis deep analysis precision analysis divide-and-conquer analysis

Spread of common native and invasive grasses and ruderal trees following anthropogenic disturbances in a tropical dry forest

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Ecological Processes 2017年第1期6卷 394-407页

作者： Xavier A.Jaime Skip J.Van Bloem Frank H.Koch Stacy A.C.Nelson Department of Ecosystem Science and Management Texas A&M University2138 TAMUCollege StationTX 77843-2138USA Belle W.Baruch Institute of Coastal Ecology and Forest Science Clemson UniversityPO Box 596GeorgetownSC 29442USA USDA Forest Service Southern Research Station3041 East Cornwallis RoadResearch Triangle ParkNC 27709USA Center for geospatial analytics Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighNC 27695USA.

Introduction:A fundamental challenge to the integrity of tropical dry forest ecosystems is the invasion of nonnative grass species.These grasses compete for resources and fuel anthropogenic wildfires.In 2012,a bulldozer from the Puerto Rico Electric Power Authority cleared a 570-m trail from a state road into a mature dry forest section of Guánica Forest to control a wildfire.We monitored colonization by a non-native invasive grass(Megathyrsus maximus),a highly invasive tree(Leucaena leucocephala),and a native grass(Uniola virgata),as well as natural regeneration,along the bulldozer trail.We determined whether bulldozing facilitated colonization by these species into the forest and the extent of spread.Results:Distance from propagule source and temporal variations strongly influenced colonization by our three focal species.Megathyrsus maximus invaded along the trail from source populations by the state road.The establishment of new colonies of M.maximus seedlings went as far as 570 m inside the forest(i.e.,at the end of the bulldozer trail),but we found most new colonies within 270 m of the road.Leucaena leucocephala exhibited a similar spreading pattern.Before disturbance,Uniola virgata was distributed widely across the forest,but the highest densities were found in areas near the latter portion(>401 m)of the bulldozer trail.Subsequently,the species formed new clumps along more than half of the trail(250 to 570 m),apparently colonizing from undisturbed patches nearby.Conclusions:Bulldozing facilitated the invasion of non-native vegetation.The projected community assemblage will be more fire-prone than before since M.maximus carries fire across the landscape better than U.virgata,emphasizing the capacity of invasive plant colonization to alter local ecological processes after only a single wildfire and bulldoze event.Our results provide a valuable baseline for short-term vegetation response to anthropogenic disturbances in tropical semi-deciduous dry forests.

关键词： Tropical dry forest Grass invasions Bulldozing Wildfire Leucaena leucocephala Megathyrsus(Panicum)maximus Uniola virgata