The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Atanu Kumar Pati - One of the best experts on this subject based on the ideXlab platform.
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A Population Estimation study reveals a staggeringly high number of cattle on the streets of urban Raipur in India.
PloS one, 2021Co-Authors: Bhupendra Kumar Sahu, Arti Parganiha, Atanu Kumar PatiAbstract:Cattle are cosmopolitan in distribution. They are economically and ecologically significant. The cattle menace on the urban streets of developing and underdeveloped countries is challenging. The number of road accidents is increasing rapidly over time, in the urban areas of most of the developing countries, like India. In the present study, we estimated the Population of cattle wandering on the streets/roads/highways of Raipur city of India using the direct headcount method and advanced Photographic Capture-Recapture Method (PCRCM). We compared these two methods of Population Estimation to check their suitability and adequacy. We superimposed 163 grids (1.0 x 1.0 km each) on the map of Raipur city using Quantum Geographic Information System (QGIS) software. We randomly selected 20 grids for the Estimation of the street cattle Population. We used both line transect and block count sampling techniques under the direct headcount method. The estimates of visibly roaming cattle on the Raipur city streets were 11808.45 and 11198.30 using the former and the latter sampling techniques, respectively. Further, advanced PCRCM indicated an estimated 35149.61 and 34623.20 cattle using the line transect and block counting sampling techniques, respectively. We observed a female-biased sex ratio in both mature and immature cattle. The frequency of mature cattle was significantly higher than that of naive cattle, followed by the calf. Further, we noticed the frequency of cattle in a grid in the following order: cow > bull > heifer > immature male > female calf > male calf. We concluded that the estimated Population of street cattle in Raipur city is about 35 thousand. The results of both the techniques, i.e., direct headcount method and PCRCM, are consistent for Population Estimation. The direct headcount method yields the number of cattle visibly roaming on the street at a particular time. In contrast, advanced PCRCM gives the total Population of street cattle in the city. Active surveillance of the urban cattle Population might be of critical importance for municipal and city planners. A better understanding of the urban cattle Population might help mitigate the cattle menace on the street, eventually preventing cattle-human conflict and minimizing road accidents. The techniques adopted in this study will also help estimate the Population of free-ranging dogs and other wildlife animals in any target location.
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Population Estimation study reveals staggeringly high number of cattle on the streets of urban Raipur, Chhattisgarh, India
2020Co-Authors: Bhupendra Kumar Sahu, Arti Parganiha, Atanu Kumar PatiAbstract:Cattle (bovine species) are economically and ecologically very important and are cosmopolitan in distribution. Increasing number of cattle on the urban streets of developing and underdeveloped countries has become an unmanageable menace in recent time. Consequently numbers of road accidents have increased in the urban areas of most of the developing countries, like India. In the present study, we estimated the Population of street cattle wandering on the street/road/highway of Raipur city of India using direct head count method and advanced Photographic Capture Recapture Method (PCRCM). We compared these two scientific methods of Population Estimation to check their adequacy. We prepared grid (1.0 x 1.0 km) on the map of Raipur city using Quantum Geographic Information System (QGIS) software and randomly selected 20 grids for the Estimation of street cattle Population. We used line transects and block count methods for data sampling. Results of direct head count method indicated an Estimation of 11808.45 cattle (using line transects sampling method) and 11198.30 cattle (using block counting sampling method) visibly roaming on the street of Raipur city. Further, advanced PCRCM indicated an Estimation of 35149.61 cattle using line transects sampling method and 34623.20 cattle using block counting sampling method. We observed female biased sex ratio in both mature and immature cattle. Frequency of mature cattle was significantly higher than that of immature cattle followed by calves. Further, the frequency of cattle in a grid was found in the following order: cow > bull > heifer > immature male > female calve > male calve. We concluded that the estimated Population of street cattle in Raipur city is about 34623. Results of both the techniques, i.e., direct head count method and PCRCM for Population Estimation are consistent. The direct head count method yields the number of cattle visibly roaming on the street in a particular time; whereas advanced PCRCM gives the total Population of street cattle in the city. Results of this study might be helpful in the management of street cattle menace in urban habitat and landscape.
Qihao Weng - One of the best experts on this subject based on the ideXlab platform.
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Population Estimation of Urban Residential Communities Using Remotely Sensed Morphologic Data
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Yanhua Xie, Anthea Weng, Qihao WengAbstract:Fine-scale Population Estimation in urban areas provides information useful in such fields as emergency response, epidemiological applications, and urban management. It is however a challenge because of lack of detailed building morphologic information. This research investigated the capability of LiDAR data for extraction of residential buildings and used the results for Population Estimation in heterogeneous environments in Indianapolis, USA. A morphological building detection algorithm was applied, to extract buildings from LiDAR point cloud, and yielded an overall detection accuracy of 95%. Extracted buildings were then categorized into nonresidential buildings, apartments, single-family houses, and other buildings based on selected geometric features (e.g., area, height, and volume) and background characteristics (vegetation and impervious cover) by a random forest classifier. Linear regression modeling, based on area, volume, and housing units, was applied to examine the relationship between census Population and LiDAR-derived residential variables. The results show that morphological metrics extracted from LiDAR can be applied to classify buildings with relatively high accuracy, with an overall accuracy of 81.67%. The shape indexes contributed mostly to the residential building extraction followed by building background metrics. By excluding nonresidential buildings, the accuracy of Population Estimation increased from an RMSE of 20 and a mean absolute relative error (MARE) of 61.38% to an RMSE of 13 and a MARE of 33.52%. The differentiation between single-family houses and apartments contributed to the improved Estimation. Additionally, the introduction of building height resulted in relatively accurate unit-based Estimation. This study provides important insights into fine-scale Population Estimation in heterogeneous urban regions, when detailed building information is unavailable.
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Fine-scale Population Estimation: how Landsat ETM+ imagery can improve Population distribution mapping
Canadian Journal of Remote Sensing, 2010Co-Authors: Qihao WengAbstract:The spectral response based and land use based methods were compared for Population Estimation with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery for Marion County, Indiana, USA. With the spectral response based method, impervious surface and vegetation fractions and land surface temperature derived from the Landsat ETM+ thermal band along with spectral bands were tested for estimating Population density at the census block group level. With the land use based method, land use and land cover images were first extracted from Landsat ETM+ reflected bands. The Population count for the block groups was estimated based on the area of residential land use. Population was then dasymetrically redistributed onto a 30 m grid based on a land cover and land use map of the study area. A comparative analysis shows that the spectral response method produced a larger mean relative error of 237% for Population density at the block group, whereas the land use based method yielded a much better Estimation, with a mea...
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Residential Population Estimation using a remote sensing derived impervious surface approach
International Journal of Remote Sensing, 2006Co-Authors: Qihao WengAbstract:Residential Population Estimation was explored based on impervious surface coverage in Marion County, Indiana, USA. The impervious surface was developed by spectral unmixing of a Landsat Enhanced Thematic Mapper (ETM+) multispectral image. The residential impervious surface was then derived by geographic information system (GIS) overlay of residential land class and impervious surface. Regression analysis was conducted to develop Population density Estimation models. We found that the residential impervious surface‐based approach provided the best Population density Estimation result, with mean and median relative errors of 38% and 23%, respectively. An overall Population Estimation error of −0.97% was achieved.
Lin Chen - One of the best experts on this subject based on the ideXlab platform.
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From Static to Dynamic Tag Population Estimation: An Extended Kalman Filter Perspective
IEEE Transactions on Communications, 2016Co-Authors: Lin ChenAbstract:Tag Population Estimation has recently attracted significant research attention due to its paramount importance on a variety of radio frequency identification (RFID) applications. However, the existing Estimation mechanisms are proposed for the static case where tag Population remains constant, thus leaving the more challenging dynamic case unaddressed. This chapter introduces a generic framework of stable and accurate Estimation schemes based on Kalman filter for both static and dynamic RFID systems. We first model the system dynamics as discrete stochastic processes and leverage the techniques in extended Kalman filter (EKF) and cumulative sum control chart (CUSUM) to estimate tag Population for static/dynamic systems. By employing Lyapunov drift analysis, we characterise the performance of the proposed framework in terms of Estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters. The relative Estimation error is bounded and converged to zero at exponential rate.
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From Static to Dynamic Tag Population Estimation: An Extended Kalman Filter Perspective
arXiv: Systems and Control, 2015Co-Authors: Lin ChenAbstract:Tag Population Estimation has recently attracted significant research attention due to its paramount importance on a variety of radio frequency identification (RFID) applications. However, most, if not all, of existing Estimation mechanisms are proposed for the static case where tag Population remains constant during the Estimation process, thus leaving the more challenging dynamic case unaddressed, despite the fundamental importance of the latter case on both theoretical analysis and practical application. In order to bridge this gap, %based on \textit{dynamic framed-slotted ALOHA} (DFSA) protocol, we devote this paper to designing a generic framework of stable and accurate tag Population Estimation schemes based on Kalman filter for both static and dynamic RFID systems. %The objective is to devise Estimation schemes and analyze the boundedness of Estimation error. Technically, we first model the dynamics of RFID systems as discrete stochastic processes and leverage the techniques in extended Kalman filter (EKF) and cumulative sum control chart (CUSUM) to estimate tag Population for both static and dynamic systems. By employing Lyapunov drift analysis, we mathematically characterise the performance of the proposed framework in terms of Estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters under which our scheme can stabilise around the real Population size with bounded relative Estimation error that tends to zero with exponential convergence rate.
David Martin - One of the best experts on this subject based on the ideXlab platform.
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A Comparison of Small-Area Population Estimation Techniques Using Built-Area and Height Data, Riyadh, Saudi Arabia
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Mohammed Alahmadi, Peter M. Atkinson, David MartinAbstract:Small-area Population Estimation is important for many applications. This paper explores the usefulness of Landsat ${\bf ETM} + $ data, remotely sensed height data, census Population, and dwelling unit data to provide small-area Population estimates. Riyadh, Saudi Arabia was selected as a suitable area to test a set of methods for Population downscaling. Two broad approaches were applied: 1) statistical modeling and 2) areal interpolation. With regard to statistical modeling, regression through the origin was used to model the relationship between density of dwelling units and built area proportion at the block level and the coefficients were used to downscale the density of dwelling units to the parcel level. Areal interpolation with ancillary data (dasymetric mapping) used the block and parcel levels as the source and target zones, respectively. The Population distribution was then estimated based on the average Population per dwelling unit. Eight models were developed and tested. A conventional regression model, using only built area as a covariate, was used as a benchmark and compared with the more sophisticated models. Remotely sensed height data were used to: 1) create number of floors; 2) classify the built area into different categories; and 3) increase the user’s accuracy of the built area. It was found that remotely sensed height data were useful to explain the variation in the dependent variable across the selected study area. Dasymetric mapping was applied in order to provide a comparison, while acknowledging that the method uses Population data not available in the regression approach.
Bhupendra Kumar Sahu - One of the best experts on this subject based on the ideXlab platform.
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A Population Estimation study reveals a staggeringly high number of cattle on the streets of urban Raipur in India.
PloS one, 2021Co-Authors: Bhupendra Kumar Sahu, Arti Parganiha, Atanu Kumar PatiAbstract:Cattle are cosmopolitan in distribution. They are economically and ecologically significant. The cattle menace on the urban streets of developing and underdeveloped countries is challenging. The number of road accidents is increasing rapidly over time, in the urban areas of most of the developing countries, like India. In the present study, we estimated the Population of cattle wandering on the streets/roads/highways of Raipur city of India using the direct headcount method and advanced Photographic Capture-Recapture Method (PCRCM). We compared these two methods of Population Estimation to check their suitability and adequacy. We superimposed 163 grids (1.0 x 1.0 km each) on the map of Raipur city using Quantum Geographic Information System (QGIS) software. We randomly selected 20 grids for the Estimation of the street cattle Population. We used both line transect and block count sampling techniques under the direct headcount method. The estimates of visibly roaming cattle on the Raipur city streets were 11808.45 and 11198.30 using the former and the latter sampling techniques, respectively. Further, advanced PCRCM indicated an estimated 35149.61 and 34623.20 cattle using the line transect and block counting sampling techniques, respectively. We observed a female-biased sex ratio in both mature and immature cattle. The frequency of mature cattle was significantly higher than that of naive cattle, followed by the calf. Further, we noticed the frequency of cattle in a grid in the following order: cow > bull > heifer > immature male > female calf > male calf. We concluded that the estimated Population of street cattle in Raipur city is about 35 thousand. The results of both the techniques, i.e., direct headcount method and PCRCM, are consistent for Population Estimation. The direct headcount method yields the number of cattle visibly roaming on the street at a particular time. In contrast, advanced PCRCM gives the total Population of street cattle in the city. Active surveillance of the urban cattle Population might be of critical importance for municipal and city planners. A better understanding of the urban cattle Population might help mitigate the cattle menace on the street, eventually preventing cattle-human conflict and minimizing road accidents. The techniques adopted in this study will also help estimate the Population of free-ranging dogs and other wildlife animals in any target location.
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Population Estimation study reveals staggeringly high number of cattle on the streets of urban Raipur, Chhattisgarh, India
2020Co-Authors: Bhupendra Kumar Sahu, Arti Parganiha, Atanu Kumar PatiAbstract:Cattle (bovine species) are economically and ecologically very important and are cosmopolitan in distribution. Increasing number of cattle on the urban streets of developing and underdeveloped countries has become an unmanageable menace in recent time. Consequently numbers of road accidents have increased in the urban areas of most of the developing countries, like India. In the present study, we estimated the Population of street cattle wandering on the street/road/highway of Raipur city of India using direct head count method and advanced Photographic Capture Recapture Method (PCRCM). We compared these two scientific methods of Population Estimation to check their adequacy. We prepared grid (1.0 x 1.0 km) on the map of Raipur city using Quantum Geographic Information System (QGIS) software and randomly selected 20 grids for the Estimation of street cattle Population. We used line transects and block count methods for data sampling. Results of direct head count method indicated an Estimation of 11808.45 cattle (using line transects sampling method) and 11198.30 cattle (using block counting sampling method) visibly roaming on the street of Raipur city. Further, advanced PCRCM indicated an Estimation of 35149.61 cattle using line transects sampling method and 34623.20 cattle using block counting sampling method. We observed female biased sex ratio in both mature and immature cattle. Frequency of mature cattle was significantly higher than that of immature cattle followed by calves. Further, the frequency of cattle in a grid was found in the following order: cow > bull > heifer > immature male > female calve > male calve. We concluded that the estimated Population of street cattle in Raipur city is about 34623. Results of both the techniques, i.e., direct head count method and PCRCM for Population Estimation are consistent. The direct head count method yields the number of cattle visibly roaming on the street in a particular time; whereas advanced PCRCM gives the total Population of street cattle in the city. Results of this study might be helpful in the management of street cattle menace in urban habitat and landscape.