scholarly journals A spatial analysis for geothermal energy exploration using bivariate predictive modelling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andongma W. Tende ◽  
Mohammed D. Aminu ◽  
Jiriko N. Gajere
Keyword(s):  
Spatial Data ◽  
Frequency Ratio ◽  
Prediction Accuracy ◽  
Accuracy Assessment ◽  
Predictive Modelling ◽  
Statistical Correlation ◽  
Entropy Method ◽  
Weight Of Evidence ◽  
Statistical Index ◽  
Geothermal Resources

AbstractThe development of predictive maps for geothermal resources is fundamental for its exploration across Nigeria. In this study, spatial exploration data consisting of geology, geophysics and remote sensing was initially analysed using the Shannon entropy method to ascertain a correlation to known geothermal manifestation. The application of statistical index, frequency ratio and weight of evidence modelling was then used for integrating every predictive data for the generation of geothermal favourability maps. The receiver operating/area under curve (ROC/AUC) analysis was then employed to ascertain the prediction accuracy for all models. Basically, all spatial data displayed a significant statistical correlation with geothermal occurrence. The integration of these data suggests a high probability for geothermal manifestation within the central part of the study location. Accuracy assessment for all models using the ROC/AUC analysis suggests a high prediction capability (above 75%) for all models. Highest prediction accuracy was obtained from the frequency ratio (83.3%) followed by the statistical index model (81.3%) then the weight of evidence model (79.6%). Evidence from spatial and predictive analysis suggests geological data integration is highly efficient for geothermal exploration across the middle Benue trough.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

Accuracy assessment of a mobile terrestrial laser scanner for tree crops

2017 ◽  
Vol 8 (2) ◽  
pp. 178-182 ◽  
Author(s):  
F. H. S. Karp ◽  
A. F. Colaço ◽  
R. G. Trevisan ◽  
J. P. Molin
Keyword(s):  
Data Acquisition ◽  
Spatial Data ◽  
Accuracy Assessment ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Tree Crops ◽  
Area Estimation ◽  
Gnss Receiver ◽  
Truncated Cone ◽  
Set Up

LiDAR technology is one option to collect spatial data about canopy geometry in many crops. However, the method of data acquisition includes many errors related to the LiDAR sensor, the GNSS receiver and the data acquisition set up. Therefore, the objective of this study was to evaluate the errors involved in the data acquisition from a mobile terrestrial laser scanner (MTLS). Regular shaped objects were scanned with a developed MTLS in two different tests: i) with the system mounted on a vehicle and ii) with the system mounted on a platform running over a rail. The errors of area estimation varied between 0.001 and 0.071 m2 for the circle, square and triangle objects. The errors on volume estimations were between 0.0003 and 0.0017 m3, for cylinders and truncated cone.

scholarly journals Estimation of Building Heights and DEM Accuracy Assessment Using ICESat-2 Data Products

2021 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Goddu Pavan Sai Goud ◽  
Ashutosh Bhardwaj
Keyword(s):  
Spatial Data ◽  
Rural Areas ◽  
Urban Areas ◽  
Accuracy Assessment ◽  
Urban Expansion ◽  
Population Expansion ◽  
Cost Effective ◽  
Absolute Error ◽  
Google Earth ◽  
Surface Geometry

The use of remote sensing for urban monitoring is a very reliable and cost-effective method for studying urban expansion in horizontal and vertical dimensions. The advantage of multi-temporal spatial data and high data accuracy is useful in mapping urban vertical aspects like the compactness of urban areas, population expansion, and urban surface geometry. This study makes use of the ‘Ice, cloud, and land elevation satellite-2′ (ICESat-2) ATL 03 photon data for building height estimation using a sample of 30 buildings in three experimental sites. A comparison of computed heights with the heights of the respective buildings from google image and google earth pro was done to assess the accuracy and the result of 2.04 m RMSE was obtained. Another popularly used method by planners and policymakers to map the vertical dimension of urban terrain is the Digital Elevation Model (DEM). An assessment of the openly available DEM products—TanDEM-X and Cartosat-1 has been done over Urban and Rural areas. TanDEM-X is a German earth observation satellite that uses InSAR (Synthetic Aperture Radar Interferometry) technique to acquire DEM while Cartosat-1 is an optical stereo acquisition satellite launched by the Indian Space Research Organization (ISRO) that uses photogrammetric techniques for DEM acquisition. Both the DEMs have been compared with ICESat-2 (ATL-08) Elevation data as the reference and the accuracy has been evaluated using Mean error (ME), Mean absolute error (MAE) and Root mean square error (RMSE). In the case of Greater Hyderabad Municipal Corporation (GHMC), RMSE values 5.29 m and 7.48 m were noted for TanDEM-X 90 and CartoDEM V3 R1 respectively. While the second site of Bellampalli Mandal rural area observed 5.15 and 5.48 RMSE values for the same respectively. Therefore, it was concluded that TanDEM-X has better accuracy as compared to the CartoDEM V3 R1.

Assessment of the Contribution of Crowd Sourced Data to Post-Earthquake Building Damage Detection

2014 ◽  
Vol 6 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Reza Hassanzadeh ◽  
Zorica Nedovic-Budic
Keyword(s):  
Damage Detection ◽  
Spatial Data ◽  
Predictive Modelling ◽  
Building Damage ◽  
Crowd Sourcing ◽  
Chi Square ◽  
Data Infrastructure ◽  
Average Accuracy ◽  
Chi Square Test ◽  
Cross Tabulation

This paper compares the results of building damage detection based on Crowd Sourced (CS) data, image processing of remotely sensed (RS) data and predictive modelling with institutional spatial data (Spatial Data Infrastructure - SDI). In particular, it focuses on the contribution of Crowd Sourcing to detecting post-earthquake building damages, while also considering the integration of Crowd Sourced with two other data sources (RS and modelling). To simulate CS data submission following the 2003 earthquake in Bam City (Iran) a survey was administered to the population which experienced the earthquake. The results obtained from this and two other sources are compared with the Actual Earthquake (AE) data by cross-tabulation analysis and McNemar's Chi Square Test. When assessed against AE data, the average accuracy levels of assessments based on the use of RS data and CS data integrated with each RS data and predictive modelling and with both, show a statistically significant increase relative to the predictive modelling. While this research does not provide for a full assessment of the value of CS data alone and in fact finds it slightly inferior to predictive modelling, it suggests that Crowd Sourcing could be a useful source of information, especially if combined with other sources.

scholarly journals DO OPEN GEODATA ACTUALLY HAVE THE QUALITY THEY DECLARE? THE CASE STUDY OF MILAN, ITALY

2016 ◽  
Vol XLI-B2 ◽  
pp. 609-614
Author(s):  
M. A. Brovelli ◽  
M. Minghini ◽  
M. E. Molinari ◽  
M. Molteni
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Quality Parameters ◽  
Data Quality Control ◽  
Positional Accuracy ◽  
Systematic Assessment ◽  
Open Datasets ◽  
The Many

In the past number of years there has been an amazing flourishing of spatial data products released with open licenses. Researchers and professionals are extensively exploiting open geodata for many applications, which, in turn, include decision-making results and other (derived) geospatial datasets among their outputs. Despite the traditional availability of metadata, a question arises about the actual quality of open geodata, as their declared quality is typically given for granted without any systematic assessment. The present work investigates the case study of Milan Municipality (Northern Italy). A wide set of open geodata are available for this area which are released by national, regional and local authoritative entities. A comprehensive cataloguing operation is first performed, with 1061 geospatial open datasets from Italian providers found which highly differ in terms of license, format, scale, content, and release date. Among the many quality parameters for geospatial data, the work focuses on positional accuracy. An example of positional accuracy assessment is described for an openly-licensed orthophoto through comparison with the official, up-to-date, and large-scale vector cartography of Milan. The comparison is run according to the guidelines provided by ISO and shows that the positional accuracy declared by the orthophoto provider does not correspond to the reality. Similar results are found from analyses on other datasets (not presented here). Implications are twofold: raising the awareness on the risks of using open geodata by taking their quality for granted; and highlighting the need for open geodata providers to introduce or refine mechanisms for data quality control.

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