scholarly journals Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1696 ◽  
Author(s):  
Yongze Song ◽  
Graeme Wright ◽  
Peng Wu ◽  
Dominique Thatcher ◽  
Tom McHugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Spatial Data ◽  
Line Segment ◽  
Large Scale ◽  
Environmental Data ◽  
Road Infrastructure ◽  
Explanatory Variables ◽  
Infrastructure Performance ◽  
Pavement Damage

Road infrastructure is important to the well-being and economic health of all nations. The performance of road pavement infrastructure is sophisticated and affected by numerous factors and varies greatly across different roads. Large scale spatial analysis for assessing road infrastructure performance is increasingly required for road management, therefore multi-source factors, including satellite remotely sensed climate and environmental data, and ground-monitored vehicles observations, are collected as explanatory variables. Different from the traditional point or area based geospatial attributes, the performance of pavement infrastructure is the line segment based spatial data. Thus, a segment-based spatial stratified heterogeneity method is utilized to explore the comprehensive impacts of vehicles, climate, properties of road and socioeconomic conditions on pavement infrastructure performance. Segment-based optimal discretization is applied on discretizing segment-based pavement data, and a segment-based geographical detector is utilized to assess the spatial impacts of variables and their interactions. Results show that the segment-based methods can more reasonably and accurately describe the characteristics of line segment based spatial data and assess the spatial associations. The two major categories of factors associated with pavement damage are the variables of traffic vehicles and heavy vehicles in particular, and climate and environmental conditions. Meanwhile, the interactions between the explanatory variables in these two categories have much more influence than the single explanatory variables, and the interactions can explain more than half of the pavement damage. This study highlights the great potential of remote sensing based large scale spatial analysis of road infrastructures. The approach in this study provides new ideas for spatial analysis for segmented geographical data. The findings indicate that the quantified comprehensive impacts of variables are practical for wise decision-making for road design, construction and maintenance.

scholarly journals A Spatial Analysis Approach to Understanding Caddoan Mounds in the Arkansas River Drainage

2004 ◽  
Author(s):  
Gregory Vogel
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Large Scale ◽  
Theoretical Background ◽  
Gis Analysis ◽  
Arkansas River ◽  
Gis Modeling ◽  
River Drainage ◽  
The People ◽  
Computationally Intensive

In this article I present a theoretical framework for understanding Caddoan mounds in the central Arkansas River drainage and the implications they may hold for the social structure and environmental adaptations of the people who made them. The power and efficiency of Geographic Information Systems (GIS) modeling now allows for large-scale, computationally intensive spatial analysis simply not possible before. Questions of landscape organization or spatial relationships that previously would have taken months or even years to answer can now be solved in a matter of minutes with GIS and related technologies, given the appropriate datasets. Quite importantly, though, such analyses must first be placed in context and theory if they are to be meaningful additions to our understanding of the past. While it is conventional to refer to “GIS analysis” (and I use the term in this article), it is important to keep in mind that data manipulations alone are not analysis. GIS, along with statistical software and related computer technologies, are tools of spatial analysis just as shovels and trowels are tools of excavation. Such tools can organize and reveal information if they are employed carefully, but the tools themselves have no agency and cannot interpret anything on their own. The terms “GIS analysis” or “GIS interpretation” are therefore somewhat misnomers, just as “trowel analysis” or “trowel interpretation” would be. It is not the GIS, or any component of it, that does the analysis or interpretation; it simply manipulates spatial data. We interpret these manipulations based upon theoretical background, previous research, and the questions we wish to answer.

When Spatial Analysis Meets OLAP

2010 ◽  
Vol 6 (4) ◽  
pp. 33-60 ◽  
Author(s):  
Sandro Bimonte ◽  
Anne Tchounikine ◽  
Maryvonne Miquel ◽  
François Pinet
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Spatial Dimension ◽  
Geographic Information ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Objects ◽  
Semantic Component ◽  
Venetian Lagoon ◽  
Associated Operators

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this paper, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCube’s algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this paper, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

Continent-wide bimonthly mapping of Antarctic surface meltwater using Google Earth Engine

2021 ◽  
Author(s):  
Peter Tuckett ◽  
Jeremy Ely ◽  
Andrew Sole ◽  
Stephen Livingstone ◽  
James Lea
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Mass Balance ◽  
Large Scale ◽  
Ice Sheet ◽  
Google Earth ◽  
Landsat 8 ◽  
Supraglacial Lakes ◽  
Google Earth Engine ◽  
Temporal And Spatial

<p>Surface meltwater is widespread around the margin of the Antarctic Ice Sheet during the austral summer. This meltwater, typically transported via surface streams and rivers and stored in supraglacial lakes, has the potential to influence ice-sheet mass balance through ice-dynamic and albedo feedbacks. To predict the impact that surface melt will have on mass balance over coming decades, it is important to understand spatial and temporal variability in surface meltwater extent. A variety of methods have been used to detect supraglacial lakes in Antarctica, yet a multi-annual, continent-wide study of Antarctic supraglacial meltwater has yet to be conducted. Cloud-based computational platforms, such as Google Earth Engine (GEE), enable large-scale temporal and spatial analysis of remote sensing datasets at minimal time expense. Here, we implement an automated method for meltwater detection in GEE to generate continent-wide, bimonthly repeat assessments of supraglacial lake extent between 2013 and 2020. We use a band-threshold based approach to delineate surface water from Landsat-8 imagery. Furthermore, our method incorporates a novel technique for quantifying meltwater extent that accounts for variability in optical image coverage and cloud cover, enabling an upper uncertainty bound to be attached to minimum mapped lake areas. We present results from continent-wide mapping, and highlight initial findings that indicate evolution of lakes in Antarctica over the past seven years. This work demonstrates how platforms such as GEE have revolutionized our ability to undertake large-scale projects from remote sensing datasets, allowing for greater temporal and spatial analysis of cryospheric processes than previously possible.</p>

scholarly journals Clarifying regional hydrologic controls of the Marañón River, Peru through rapid assessment to inform system-wide basin planning approaches

Elem Sci Anth ◽  
2018 ◽  
Vol 6 ◽  
Author(s):  
Alice F. Hill ◽  
Robert F. Stallard ◽  
Karl Rittger
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Rapid Assessment ◽  
Rapid Change ◽  
Dry Season ◽  
Environmental Data ◽  
Wet Season ◽  
River Continuum ◽  
Hydropower Development ◽  
Address Data

We use remote sensing to enhance the interpretation of the first baseline dataset of hydrologic, isotopic and hydrochemical variables spanning 620 km of the upper Marañón River, in Andean Peru, from the steep alpine canyons to the lower lying jungle. Remote, data-scarce river systems are under increased hydropower development pressure to meet rising energy demands. The upstream-downstream river continuum, which serves as a conduit for resource exchange across ecosystems, is at risk, potentially endangering the people, environments, and economies that rely on river resources. The Marañón River, one of the final free-flowing headwater connections between the Andes and the Amazon, is the subject of myriad large-scale hydropower proposals. Due to challenging access, environmental data are scarce in the upper Marañón, limiting our ability to do system-wide river basin planning. We capture key processes and transitions in the context of hydropower development. Two hydrologic regimes control the Marañón dry-season flow: in the higher-elevation upper reaches, a substantial baseflow is fed by groundwater recharged from wet season rains, in contrast to the lower reaches where the mainstem discharge is controlled by rain-fed tributaries that receive rain from lowland Amazon moisture systems. Sustainability of the upper corridor’s dry-season baseflow appears to be more highly connected to the massive natural storage capacity of extensive wetlands in the puna (alpine grasslands) than with cryospheric water inputs. The extent and conservation of puna ecosystems and glacier reservoirs may be interdependent, bringing to bear important conservation questions in the context of changing climate and land use in the region. More generally, this case study demonstrates an efficient combined remote sensing and field observation approach to address data scarcity across regional scales in mountain basins facing imminent rapid change.

When Spatial Analysis Meets OLAP

2011 ◽  
pp. 249-277 ◽  
Author(s):  
Sandro Bimonte ◽  
Anne Tchounikine ◽  
Maryvonne Miquel ◽  
François Pinet
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Spatial Dimension ◽  
Geographic Information ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Objects ◽  
Semantic Component ◽  
Venetian Lagoon ◽  
Associated Operators

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this chapter, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCube’s algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this chapter, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

scholarly journals External-Memory Algorithms for Processing Line Segments in Geographic Information Systems

1996 ◽  
Vol 3 (12) ◽  
Author(s):  
Lars Arge ◽  
Darren E. Vengroff ◽  
Jeffery S. Vitter
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Line Segment ◽  
Geographical Information Systems ◽  
Large Scale ◽  
Geographical Information ◽  
External Memory ◽  
External Memory Algorithms ◽  
Line Segments ◽  
Line Segment Intersection

In the design of algorithms for large-scale applications it is essential to consider the problem of minimizing I/O communication. Geographical information systems (GIS) are good examples of such large-scale applications as they frequently handle huge amounts of spatial data. In this paper we develop efficient new external-memory algorithms for a number of important problems involving line segments in the plane, including trapezoid decomposition, batched planar point location, triangulation, red-blue line segment intersection reporting, and general line segment intersection reporting. In GIS systems, the first three problems are useful for rendering and modeling, and the latter two are frequently used for overlaying maps and extracting information<br />from them.

scholarly journals Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

2021 ◽  
Vol 13 (20) ◽  
pp. 4128
Author(s):  
Jinwen Xu ◽  
Yi Qiang
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Low Cost ◽  
Community Resilience ◽  
Hurricane Sandy ◽  
Future Research ◽  
Remote Sensing Images ◽  
Community Recovery ◽  
Human Dynamics ◽  
Explanatory Variables

Quantitative assessment of community resilience is a challenge due to the lack of empirical data about human dynamics in disasters. To fill the data gap, this study explores the utility of nighttime lights (NTL) remote sensing images in assessing community recovery and resilience in natural disasters. Specifically, this study utilized the newly-released NASA moonlight-adjusted SNPP-VIIRS daily images to analyze spatiotemporal changes of NTL radiance in Hurricane Sandy (2012). Based on the conceptual framework of recovery trajectory, NTL disturbance and recovery during the hurricane were calculated at different spatial units and analyzed using spatial analysis tools. Regression analysis was applied to explore relations between the observed NTL changes and explanatory variables, such as wind speed, housing damage, land cover, and Twitter keywords. The result indicates potential factors of NTL changes and urban-rural disparities of disaster impacts and recovery. This study shows that NTL remote sensing images are a low-cost instrument to collect near-real-time, large-scale, and high-resolution human dynamics data in disasters, which provide a novel insight into community recovery and resilience. The uncovered spatial disparities of community recovery help improve disaster awareness and preparation of local communities and promote resilience against future disasters. The systematical documentation of the analysis workflow provides a reference for future research in the application of SNPP-VIIRS daily images.

ANALISIS KESESUAIAN LAHAN DAN EVALUASI JENIS TANAH DALAM BUDIDAYA TANAMAN TEBU UNTUK PENGEMBANGAN DAERAH KABUPATEN TEGAL

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Daru Mulyono
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Spatial Analysis ◽  
Geographical Information System ◽  
Plant Productivity ◽  
Saccharum Officinarum ◽  
Geographical Information ◽  
Sugarcane Plant ◽  
Plant Cultivation ◽  
Suitability Map

The objectives of the research were to make land suitability map for sugarcane plant (Saccharum officinarum), to give recommendation of location including area for sugarcane plant cultivation and to increase sugarcane plant productivity. The research used maps overlay and Geographical Information System (GIS) which used Arch-View Spatial Analysis version 2,0 A in Remote Sensing Laboratory, Agency for the Assessment and Application of Technology (BPPT), Jakarta. The research was carried out in Tegal Regency starting from June to October 2004.The results of the research showed that the suitable, conditionally suitable, and not suitable land for sugarcane cultivation in Tegal Regency reached to a high of 20,227 ha, 144 ha, and 81,599 ha respectively. There were six most dominant kind of soil: alluvial (32,735 ha), grumosol 5,760 ha), mediteran (17,067 ha), latosol   (18,595 ha), glei humus (596 ha), and regosol (22,721 ha).

Giscience in the Mountain Geography

2010 ◽  
Vol 27 (1-2) ◽  
pp. 81-90
Author(s):  
Krishna Poudel
Keyword(s):  
Spatial Data ◽  
Environmental Data ◽  
Temporal Data ◽  
Observation Systems ◽  
Research Findings ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
Mountain Geography ◽  
Different Sources

Mountains have distinct geography and are dynamic in nature compared to the plains. 'Verticality' and 'variation' are two fundamental specificities of the mountain geography. They possess distinct temporal and spatial characteristics in a unique socio-cultural setting. There is an ever increasing need for spatial and temporal data for planning and management activities; and Geo Information (GI) Science (including Geographic Information and Earth Observation Systems). This is being recognized more and more as a common platform for integrating spatial data with social, economic and environmental data and information from different sources. This paper investigates the applicability and challenges of GISscience in the context of mountain geography with ample evidences and observations from the mountain specific publications, empirical research findings and reports. The contextual explanation of mountain geography, mountain specific problems, scientific concerns about the mountain geography, advances in GIScience, the role of GIScience for sustainable development, challenges on application of GIScience in the contexts of mountains are the points of discussion. Finally, conclusion has been made with some specific action oriented recommendations.

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