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Lacking coastal and offshore wind speed time series of sufficient length, reanalysis data and wind speed models serve as the primary sources of valuable information for wind power management. In this study, long-length observational records and modelled data from Uncertainties in Ensembles of Regional Re-Analyses system are collected, analyzed and modelled. The first stage refers to the statistical analysis of the time series marginal structure in terms of the fitting accuracy, the distributions’ tails behavior, extremes response and the power output errors, using Weibull distribution and three parameter Weibull-related distributions (Burr Type III and XII, Generalized Gamma). In the second stage, the co-located samples in time and space are compared in order to investigate the reanalysis data performance. In the last stage, the stochastic generation mathematical framework is applied based on a Generalized Hurst-Kolmogorov process embedded in a Symmetric-Moving-Average scheme, which is used for the simulation of a wind process while preserving explicitly the marginal moments, wind’s intermittency and long-term persistence. Results indicate that Burr and Generalized Gamma distribution could be successfully used for wind resource assessment, although, the latter emerged enhanced performance in most of the statistical tests. Moreover, the credibility of the reanalysis data is questionable due to increased bias and root mean squared errors, however, high-order statistics along with the long-term persistence are thoroughly preserved. Eventually, the simplicity and the flexibility of the stochastic generation scheme to reproduce the seasonal and diurnal wind characteristics by preserving the long-term dependence structure are highlighted.
Loukas Katikas; Panayiotis Dimitriadis; Demetris Koutsoyiannis; Themistoklis Kontos; Phaedon Kyriakidis. A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series. Applied Energy 2021, 295, 116873 .
AMA StyleLoukas Katikas, Panayiotis Dimitriadis, Demetris Koutsoyiannis, Themistoklis Kontos, Phaedon Kyriakidis. A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series. Applied Energy. 2021; 295 ():116873.
Chicago/Turabian StyleLoukas Katikas; Panayiotis Dimitriadis; Demetris Koutsoyiannis; Themistoklis Kontos; Phaedon Kyriakidis. 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series." Applied Energy 295, no. : 116873.
Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.
Yeseul Kim; Phaedon C. Kyriakidis; No-Wook Park. A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions. Remote Sensing 2020, 12, 1553 .
AMA StyleYeseul Kim, Phaedon C. Kyriakidis, No-Wook Park. A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions. Remote Sensing. 2020; 12 (10):1553.
Chicago/Turabian StyleYeseul Kim; Phaedon C. Kyriakidis; No-Wook Park. 2020. "A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions." Remote Sensing 12, no. 10: 1553.
The smart city notion provides an integrated and systematic answer to challenges facing cities today. Smart city policy makers and technology vendors are increasingly stating their interest in human-centered smart cities. On the other hand, in many studies smart city policies bring forward a one-size-fits-all type of recommendation for all areas in question instead of location-specific ones. Based on the above considerations, this paper illustrates that smart citizen characteristics, alongside local urban challenges, are paving the way towards more effective efforts in smart city policy decision making. Our main presumption is that the development level of human-centered indicators of smart cities varies locally. The scientific objective of this paper is to find a simple, understandable link between human smart characteristics and local determinants in Limassol city, Cyprus. The data set consists of seven indicators defined as human smart characteristics and seven which determine local urban challenges consisting of demographic dynamics and built infrastructure attributes based on housing. Correlations of the 14 above indicators are examined in entirety and separately, as the study area was divided into three spatial sub-groups (high, moderate, and low coverage areas) depending on dispersed urbanization, as the main challenge of the study area. The data were obtained mainly from the most recent population census in 2011 and categorized in sub-groups by triggering CLC 2012. Analyzing the statistics using principal component analysis (PCA), we identify significant relationships between human smart city characteristics, demographic dynamics and built infrastructure attributes which can be used in local policy decision making. Spatial variations based on the dispersed urbanization are also observed regarding the above-mentioned relationships.
Maroula N. Alverti; Kyriakos Themistocleous; Phaedon C. Kyriakidis; Diofantos G. Hadjimitsis. A Study of the Interaction of Human Smart Characteristics with Demographic Dynamics and Built Environment: The Case of Limassol, Cyprus. Smart Cities 2020, 3, 48 -73.
AMA StyleMaroula N. Alverti, Kyriakos Themistocleous, Phaedon C. Kyriakidis, Diofantos G. Hadjimitsis. A Study of the Interaction of Human Smart Characteristics with Demographic Dynamics and Built Environment: The Case of Limassol, Cyprus. Smart Cities. 2020; 3 (1):48-73.
Chicago/Turabian StyleMaroula N. Alverti; Kyriakos Themistocleous; Phaedon C. Kyriakidis; Diofantos G. Hadjimitsis. 2020. "A Study of the Interaction of Human Smart Characteristics with Demographic Dynamics and Built Environment: The Case of Limassol, Cyprus." Smart Cities 3, no. 1: 48-73.
The impact of medium-sized southern European cities challenges on the “smartness” of the city is a quite interesting case that is not quite analyzed yet. Our scientific objective is to find a simple understandable model linking human smart characteristics to a group of socio-demographic and urban environment indices, applied to the case of Limassol Urban Complex, the southernmost European city, with a total population of 208 980. The data set of the analysis contains 25 variables in 3 thematic domains using as spatial analysis level, the 126 postal code areas of the most urbanized part of the city. The study results obtained through multivariate statistical analysis and thematic cartography using GIS technology. The results reveal that the human smart characteristics consist of the use of high-speed internet and broad band telephony, recycling activities, employment in creative industry, high educational attainment and open-mindedness (i.e. participation in EU elections), are significantly correlated with demographic dynamics and built infrastructure characteristics. Creativity and open-mindedness tend to appear in most densely urban areas, mostly occupied by indigenous inhabitants. Recycling and technology oriented smart characteristics are mostly correlated with no-native residents, and high educational attainment. In the outskirts of the city of Limassol the developing dynamics are almost the same with a greater blend between native and non-native inhabitants.
Maroula Alverti; Kyriacos Themistocleous; Phaedon C. Kyriakidis; Diofantos G. Hadjimitsis. A Human Centric Approach on the Analysis of the Smart City Concept: the case study of the Limassol city in Cyprus. Advances in Geosciences 2018, 45, 305 -320.
AMA StyleMaroula Alverti, Kyriacos Themistocleous, Phaedon C. Kyriakidis, Diofantos G. Hadjimitsis. A Human Centric Approach on the Analysis of the Smart City Concept: the case study of the Limassol city in Cyprus. Advances in Geosciences. 2018; 45 ():305-320.
Chicago/Turabian StyleMaroula Alverti; Kyriacos Themistocleous; Phaedon C. Kyriakidis; Diofantos G. Hadjimitsis. 2018. "A Human Centric Approach on the Analysis of the Smart City Concept: the case study of the Limassol city in Cyprus." Advances in Geosciences 45, no. : 305-320.
In earth and environmental sciences applications, uncertainty analysis regarding the outputs of models whose parameters are spatially varying (or spatially distributed) is often performed in a Monte Carlo framework. In this context, alternative realizations of the spatial distribution of model inputs, typically conditioned to reproduce attribute values at locations where measurements are obtained, are generated via geostatistical simulation using simple random (SR) sampling. The environmental model under consideration is then evaluated using each of these realizations as a plausible input, in order to construct a distribution of plausible model outputs for uncertainty analysis purposes. In hydrogeological investigations, for example, conditional simulations of saturated hydraulic conductivity are used as input to physically-based simulators of flow and transport to evaluate the associated uncertainty in the spatial distribution of solute concentration. Realistic uncertainty analysis via SR sampling, however, requires a large number of simulated attribute realizations for the model inputs in order to yield a representative distribution of model outputs; this often hinders the application of uncertainty analysis due to the computational expense of evaluating complex environmental models. Stratified sampling methods, including variants of Latin hypercube sampling, constitute more efficient sampling aternatives, often resulting in a more representative distribution of model outputs (e.g., solute concentration) with fewer model input realizations (e.g., hydraulic conductivity), thus reducing the computational cost of uncertainty analysis. The application of stratified and Latin hypercube sampling in a geostatistical simulation context, however, is not widespread, and, apart from a few exceptions, has been limited to the unconditional simulation case. This paper proposes methodological modifications for adopting existing methods for stratified sampling (including Latin hypercube sampling), employed to date in an unconditional geostatistical simulation context, for the purpose of efficient conditional simulation of Gaussian random fields. The proposed conditional simulation methods are compared to traditional geostatistical simulation, based on SR sampling, in the context of a hydrogeological flow and transport model via a synthetic case study. The results indicate that stratified sampling methods (including Latin hypercube sampling) are more efficient than SR, overall reproducing to a similar extent statistics of the conductivity (and subsequently concentration) fields, yet with smaller sampling variability. These findings suggest that the proposed efficient conditional sampling methods could contribute to the wider application of uncertainty analysis in spatially distributed environmental models using geostatistical simulation.
Stelios Liodakis; Phaedon Kyriakidis; Petros Gaganis. Conditional Latin Hypercube Simulation of (Log)Gaussian Random Fields. Mathematical Geosciences 2017, 50, 127 -146.
AMA StyleStelios Liodakis, Phaedon Kyriakidis, Petros Gaganis. Conditional Latin Hypercube Simulation of (Log)Gaussian Random Fields. Mathematical Geosciences. 2017; 50 (2):127-146.
Chicago/Turabian StyleStelios Liodakis; Phaedon Kyriakidis; Petros Gaganis. 2017. "Conditional Latin Hypercube Simulation of (Log)Gaussian Random Fields." Mathematical Geosciences 50, no. 2: 127-146.
The paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models.
Seda Şalap-Ayça; Piotr Jankowski; Keith C Clarke; Phaedon C Kyriakidis; Atsushi Nara. A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model. International Journal of Geographical Information Science 2017, 32, 637 -662.
AMA StyleSeda Şalap-Ayça, Piotr Jankowski, Keith C Clarke, Phaedon C Kyriakidis, Atsushi Nara. A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model. International Journal of Geographical Information Science. 2017; 32 (4):637-662.
Chicago/Turabian StyleSeda Şalap-Ayça; Piotr Jankowski; Keith C Clarke; Phaedon C Kyriakidis; Atsushi Nara. 2017. "A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model." International Journal of Geographical Information Science 32, no. 4: 637-662.
Phaedon Kyriakidis. Aggregate Data: Geostatistical Solutions for Reconstructing Attribute Surfaces. Encyclopedia of GIS 2017, 49 -59.
AMA StylePhaedon Kyriakidis. Aggregate Data: Geostatistical Solutions for Reconstructing Attribute Surfaces. Encyclopedia of GIS. 2017; ():49-59.
Chicago/Turabian StylePhaedon Kyriakidis. 2017. "Aggregate Data: Geostatistical Solutions for Reconstructing Attribute Surfaces." Encyclopedia of GIS , no. : 49-59.
With their increasing availability and quantity, remote sensing images have become an invaluable data source for geographic research and beyond. The detection and analysis of spatial patterns from such images and other kinds of geographic fields, constitute a core aspect of Geographic Information Science. Per-cell analysis, where one cell’s characteristics are considered (geo-atom), and interaction-based analysis, where pairwise spatial relationships are considered (geo-dipole), have been widely applied to discover patterns. However, both can only characterize simple spatial patterns, such as global (overall) statistics, e.g., attribute average, variance, or pairwise auto-correlation. Such statistics alone cannot capture the full complexity of urban or natural structures embedded in geographic fields. For example, empirical (sample) correlation functions established from visually different patterns may have similar shapes, sills, and ranges. Higher-order analyses are therefore required to address this shortcoming. This work investigates the necessity and feasibility of extending the geo-dipole to a new construct, the geo-multipole, in which attribute values at multiple (more than two) locations are simultaneously considered for uncovering spatial patterns that cannot be extracted otherwise. We present experiments to illustrate the advantage of the geo-multipole over the geo-dipole in terms of quantifying spatial patterns in geographic fields. In addition, we highlight cases where two-point measures of spatial association alone are not sufficient to describe complex spatial patterns; for such cases, the geo-multipole and multiple-point (geo)statistics provide a richer analytical framework.
Rui Zhu; Phaedon C. Kyriakidis; Krzysztof Janowicz. Beyond Pairs: Generalizing the Geo-dipole for Quantifying Spatial Patterns in Geographic Fields. Lecture Notes in Geoinformation and Cartography 2017, 331 -348.
AMA StyleRui Zhu, Phaedon C. Kyriakidis, Krzysztof Janowicz. Beyond Pairs: Generalizing the Geo-dipole for Quantifying Spatial Patterns in Geographic Fields. Lecture Notes in Geoinformation and Cartography. 2017; ():331-348.
Chicago/Turabian StyleRui Zhu; Phaedon C. Kyriakidis; Krzysztof Janowicz. 2017. "Beyond Pairs: Generalizing the Geo-dipole for Quantifying Spatial Patterns in Geographic Fields." Lecture Notes in Geoinformation and Cartography , no. : 331-348.
This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges.
No-Wook Park; Phaedon C. Kyriakidis; Sungwook Hong. Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions. Remote Sensing 2017, 9, 255 .
AMA StyleNo-Wook Park, Phaedon C. Kyriakidis, Sungwook Hong. Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions. Remote Sensing. 2017; 9 (3):255.
Chicago/Turabian StyleNo-Wook Park; Phaedon C. Kyriakidis; Sungwook Hong. 2017. "Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions." Remote Sensing 9, no. 3: 255.
The Cancer Registry of Crete is a regional population database that collects cancer morbidity/mortality data along with several risk factors. The current study assessed the geographical variation of lung cancer among ever and never smokers in Crete during the last 20 years. Lung cancer patient records (1992-2013) including information on medical history and smoking habits were obtained from the Cancer Registry of Crete. Age-Adjusted Incidence Rates (AAIR), prevalence of smoking among lung cancer patients and the Population-Attributable Fraction (PAF%) of tobacco smoking were estimated. Kaplan-Meier curves, grouped per smoking status were constructed, and spatio-temporal analyses were carried out to assess the geographical variations of lung cancer and smoking (a = 0.05). New lung cancer cases in Crete accounted for 9% of all cancers (AAIRboth genders = 40.2/100,000/year, AAIRmales = 73.1/100,000/year, AAIRfemales = 11.8/100,000/year). Ever smokers presented significantly higher incidence compared to ex-smokers (p = 0.02) and never smokers (p < 0.001). The highest increase was observed in ever smokers (AAIR1992 = 19.2/100,000/year, AAIR2013 = 25.4/100,000/year, p = 0.03), while never smokers presented the lowest increase from 1992 to 2013 (AAIR1992 = 5.3/100,000/year, AAIR2013 = 6.8/100,000/year, p = 0.2). The PAF% of lung cancer mortality is 86% for both genders (males: 89%, females: 78%). AAIRs ranged from 25 to 50/100,000/year, while significant geographical differences were observed among the municipalities of Crete (p = 0.02). Smokers living in the south-east urban regions presented higher risk of dying from lung cancer (RR = 2.2; 95%CI = 1.3-3.5). The constant increase of lung cancer rates among both genders, especially in females, outlines the need for targeted, geographically-oriented, life-style preventive measures. Design of population-based screening programs, tobacco awareness campaigns and smoking cessation programs in lung cancer hot spots could be guide by these findings.
Dimitra Sifaki-Pistolla; C. Lionis; V. Georgoulias; P. Kyriakidis; F. Koinis; S. Aggelaki; N. Tzanakis. Lung cancer and tobacco smoking in Crete, Greece: reflections from a population-based cancer registry from 1992 to 2013. Tobacco Induced Diseases 2017, 15, 6 .
AMA StyleDimitra Sifaki-Pistolla, C. Lionis, V. Georgoulias, P. Kyriakidis, F. Koinis, S. Aggelaki, N. Tzanakis. Lung cancer and tobacco smoking in Crete, Greece: reflections from a population-based cancer registry from 1992 to 2013. Tobacco Induced Diseases. 2017; 15 (1):6.
Chicago/Turabian StyleDimitra Sifaki-Pistolla; C. Lionis; V. Georgoulias; P. Kyriakidis; F. Koinis; S. Aggelaki; N. Tzanakis. 2017. "Lung cancer and tobacco smoking in Crete, Greece: reflections from a population-based cancer registry from 1992 to 2013." Tobacco Induced Diseases 15, no. 1: 6.
Maroula Alverti; Diofantos Hadjimitsis; Phaedon Kyriakidis; Konstantinos Serraos. Smart city planning from a bottom-up approach: local communities' intervention for a smarter urban environment. Fourth International Conference on Remote Sensing and Geoinformation of the Environment 2016, 968819 -968819-12.
AMA StyleMaroula Alverti, Diofantos Hadjimitsis, Phaedon Kyriakidis, Konstantinos Serraos. Smart city planning from a bottom-up approach: local communities' intervention for a smarter urban environment. Fourth International Conference on Remote Sensing and Geoinformation of the Environment. 2016; ():968819-968819-12.
Chicago/Turabian StyleMaroula Alverti; Diofantos Hadjimitsis; Phaedon Kyriakidis; Konstantinos Serraos. 2016. "Smart city planning from a bottom-up approach: local communities' intervention for a smarter urban environment." Fourth International Conference on Remote Sensing and Geoinformation of the Environment , no. : 968819-968819-12.
This article presents a geostatistical approach for downscaling precipitation products from passive microwave satellites with geostationary Meteorological Satellite observations. More precisely, the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation (daily level 3 product) with 0.25° spatial resolution and the Communication, Ocean and Meteorological Satellite (COMS) infrared (IR) data with 5 km spatial resolution were used for the downscaling experiment over the Korean peninsula. Brightness temperature data observed at COMS IR 1 and water vapour channels were incorporated for downscaling via area-to-point residual Kriging with non-linear regression. The evaluation results with densely sampled Automatic Weather Station data revealed that incorporating the COMS IR observations with the AMSR2 precipitation showed similar error statistics to those of the AMSR2 precipitation because of the limitations of IR observations themselves and the inherent errors of the AMSR2 precipitation product over land. However, the area-based evaluation using information entropy indicated that incorporating the COMS observations resulted in more detailed spatial variation in the final product than direct downscaling of the AMSR2 precipitation. In addition, local precipitation patterns could be captured when there were regions with missing precipitation values in the AMSR2 product. Consequently, the downscaling result is useful for understanding the local precipitation patterns with an accuracy similar to that provided by microwave satellite observations. It is also suggested that the spatial variability in the downscaling result and errors in input low-resolution data should be considered when downscaling coarse resolution data using fine resolution auxiliary variables.
No-Wook Park; Sungwook Hong; Phaedon Kyriakidis; Woojoo Lee; Sang-Jin Lyu. Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations. International Journal of Remote Sensing 2016, 37, 3858 -3869.
AMA StyleNo-Wook Park, Sungwook Hong, Phaedon Kyriakidis, Woojoo Lee, Sang-Jin Lyu. Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations. International Journal of Remote Sensing. 2016; 37 (16):3858-3869.
Chicago/Turabian StyleNo-Wook Park; Sungwook Hong; Phaedon Kyriakidis; Woojoo Lee; Sang-Jin Lyu. 2016. "Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations." International Journal of Remote Sensing 37, no. 16: 3858-3869.
Despite growing research into the socio-economic aspects of vulnerability [1]-[4], relatively little work has linked population dynamics with climate change beyond the complex relationship between migration and climate change [5]. It is likely, however, that most people experience climate change in situ, so understanding the role of population dynamics remains critical. How a given number of people, in a given location and with varying population characteristics may exacerbate or mitigate the impacts of climate change or how, conversely, they may be vulnerable to climate change impacts are basic questions that remain largely unresolved [6]. This paper explores where and to what extent population dynamics intersect with high exposure to climate change. Specifically, in Eastern Africa's Lake Victoria Basin (LVB), a climate change/health vulnerability hotspot we have identified in prior research [7], we model child undernutrition vulnerability indices based on climate variables, including proxy measures (NDVI) derived from satellite imagery, at a 5-km spatial resolution. Results suggest that vegetation changes associated with precipitation decline in rural areas of sub-Saharan Africa can help predict deteriorating child health.
David López-Carr; Kevin M. Mwenda; Narcisa G. Pricope; Phaedon C. Kyriakidis; Marta M. Jankowska; John Weeks; Chris Funk; Gregory Husak; Joel Michaelsen. Climate-Related Child Undernutrition in the Lake Victoria Basin: An Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 2830 -2835.
AMA StyleDavid López-Carr, Kevin M. Mwenda, Narcisa G. Pricope, Phaedon C. Kyriakidis, Marta M. Jankowska, John Weeks, Chris Funk, Gregory Husak, Joel Michaelsen. Climate-Related Child Undernutrition in the Lake Victoria Basin: An Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016; 9 (6):2830-2835.
Chicago/Turabian StyleDavid López-Carr; Kevin M. Mwenda; Narcisa G. Pricope; Phaedon C. Kyriakidis; Marta M. Jankowska; John Weeks; Chris Funk; Gregory Husak; Joel Michaelsen. 2016. "Climate-Related Child Undernutrition in the Lake Victoria Basin: An Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 6: 2830-2835.
Surface modeling; Spatial Interpolation; Downscaling Geographic information systems (GIS) are routinely used to integrate different layers of geospatial information for spatial analysis and decision making. Such an integration often involves data of geospatial attributes available at different geographical units, e.g., administrative zones versus pixels of remotely sensed imagery. What is required in such cases is a transformation of attribute values from one existing spatial partition to another, that is, a change of the geographical units over which the original data were acquired with an associated change in the actual attribute values reported. In public health applications, for example, socioeconomic data reported over census tracts must be integrated with disease data available over different administrative reporting zones, to assess disease risk at increasingly finer spatial resolutions. Similarly, in remote sensing applications, reflectance data recorded by dif ...
Phaedon Kyriakidis. Aggregate Data: Geostatistical Solutions for Reconstructing Attribute Surfaces. Encyclopedia of GIS 2016, 1 -10.
AMA StylePhaedon Kyriakidis. Aggregate Data: Geostatistical Solutions for Reconstructing Attribute Surfaces. Encyclopedia of GIS. 2016; ():1-10.
Chicago/Turabian StylePhaedon Kyriakidis. 2016. "Aggregate Data: Geostatistical Solutions for Reconstructing Attribute Surfaces." Encyclopedia of GIS , no. : 1-10.
Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-derived ambiguity index with classification accuracy values at reference data locations. As for the ambiguity measure, a novel discrimination capability index (DCI) is defined from per class posteriori probabilities and then calibrated via logistic regression to derive soft probabilities. Integration of indicator-coded reference data with soft probabilities is finally carried out for mapping classification accuracy. It is demonstrated via a case study involving classification of multi-temporal and multi-sensor SAR datasets, that the proposed approach can provide a map of locally-varying accuracy values, while respecting the overall accuracy derived from the confusion matrix. It can also highlight areas where the benefit of data fusion was significant. It is expected that the indicator approach presented in this paper could be a useful methodology for assessing the spatial quality of classification results in a probabilistic way.
No-Wook Park; Phaedon C. Kyriakidis; Suk-Young Hong. Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index. Remote Sensing 2016, 8, 320 .
AMA StyleNo-Wook Park, Phaedon C. Kyriakidis, Suk-Young Hong. Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index. Remote Sensing. 2016; 8 (4):320.
Chicago/Turabian StyleNo-Wook Park; Phaedon C. Kyriakidis; Suk-Young Hong. 2016. "Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index." Remote Sensing 8, no. 4: 320.
Stelios Liodakis; Phaedon Kyriakidis; Petros Gaganis. Accounting for model sensitivity in controlled (log)Gaussian geostatistical simulation. Spatial Statistics 2015, 14, 224 -239.
AMA StyleStelios Liodakis, Phaedon Kyriakidis, Petros Gaganis. Accounting for model sensitivity in controlled (log)Gaussian geostatistical simulation. Spatial Statistics. 2015; 14 ():224-239.
Chicago/Turabian StyleStelios Liodakis; Phaedon Kyriakidis; Petros Gaganis. 2015. "Accounting for model sensitivity in controlled (log)Gaussian geostatistical simulation." Spatial Statistics 14, no. : 224-239.
Despite growing research into the socio-economic aspects of vulnerability [1-3], relatively little work has linked population dynamics with climate change. Understanding the role of population dynamics remains critical. How a given number of people, in a given location and with varying population characteristics may exacerbate or mitigate the impacts of climate change or how, conversely, they may be vulnerable to climate change impacts are basic questions that remain largely unresolved [4]. This paper explores where and to what extent population dynamics intersect with high exposure to climate change. Specifically, in Eastern Africa's Lake Victoria Basin (LVB), a climate change/health vulnerability hotspot we have identified in prior research [5], we model child malnutrition vulnerability indices based on climate variables at a 5km spatial resolution.
David Lopez-Carr; Kevin M. Mwenda; Narcisa G. Pricope; Phaedon C. Kyriakidis; Marta M. Jankowska; John Weeks; Chris Funk; Gregory Husak; Joel Michaelsen. A spatial analysis of climate-related child malnutrition in the Lake Victoria Basin. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015, 2564 -2567.
AMA StyleDavid Lopez-Carr, Kevin M. Mwenda, Narcisa G. Pricope, Phaedon C. Kyriakidis, Marta M. Jankowska, John Weeks, Chris Funk, Gregory Husak, Joel Michaelsen. A spatial analysis of climate-related child malnutrition in the Lake Victoria Basin. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015; ():2564-2567.
Chicago/Turabian StyleDavid Lopez-Carr; Kevin M. Mwenda; Narcisa G. Pricope; Phaedon C. Kyriakidis; Marta M. Jankowska; John Weeks; Chris Funk; Gregory Husak; Joel Michaelsen. 2015. "A spatial analysis of climate-related child malnutrition in the Lake Victoria Basin." 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 2564-2567.
Phaedon C. Kyriakidis; George K. Vasios; Dimitra Kitsiou. Delineating sea surface water quality regions from remotely sensed data using textural information. Remote Sensing 2015, 95351W -95351W-7.
AMA StylePhaedon C. Kyriakidis, George K. Vasios, Dimitra Kitsiou. Delineating sea surface water quality regions from remotely sensed data using textural information. Remote Sensing. 2015; ():95351W-95351W-7.
Chicago/Turabian StylePhaedon C. Kyriakidis; George K. Vasios; Dimitra Kitsiou. 2015. "Delineating sea surface water quality regions from remotely sensed data using textural information." Remote Sensing , no. : 95351W-95351W-7.
Olaf Menzer; Wendy Meiring; Phaedon C. Kyriakidis; Joseph P. McFadden. Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling. Atmospheric Environment 2015, 101, 312 -327.
AMA StyleOlaf Menzer, Wendy Meiring, Phaedon C. Kyriakidis, Joseph P. McFadden. Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling. Atmospheric Environment. 2015; 101 ():312-327.
Chicago/Turabian StyleOlaf Menzer; Wendy Meiring; Phaedon C. Kyriakidis; Joseph P. McFadden. 2015. "Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling." Atmospheric Environment 101, no. : 312-327.
Global and local (site specific) multivariate variogram and madogram measures of attribute spatial (dis)similarity are linked to multivariate extensions of Geary’s and Gini’s indices of spatial association. These measures are then employed for elucidating spatiotemporal patterns in monthly sea surface temperature and chlorophyll-a concentration data over the North Aegean Sea in Greece, derived, respectively, from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) sensor and NASA’s Sea-Viewing Wide Field-of-view Sensor (SeaWiFS).
Phaedon C Kyriakidis; Dimitra Kitsiou; Dimitris Kavroudakis. Multivariate Variogram and Madogram: Tools for Quantifying Diversity/Dissimilarity in Spatiotemporal Data. Lecture Notes in Earth System Sciences 2013, 235 -238.
AMA StylePhaedon C Kyriakidis, Dimitra Kitsiou, Dimitris Kavroudakis. Multivariate Variogram and Madogram: Tools for Quantifying Diversity/Dissimilarity in Spatiotemporal Data. Lecture Notes in Earth System Sciences. 2013; ():235-238.
Chicago/Turabian StylePhaedon C Kyriakidis; Dimitra Kitsiou; Dimitris Kavroudakis. 2013. "Multivariate Variogram and Madogram: Tools for Quantifying Diversity/Dissimilarity in Spatiotemporal Data." Lecture Notes in Earth System Sciences , no. : 235-238.