scholarly journals An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 1029
Author(s):  
Xuzhe Duan ◽  
Qingwu Hu ◽  
Pengcheng Zhao ◽  
Shaohua Wang ◽  
Mingyao Ai
Keyword(s):  
Satellite Imagery ◽  
Spatial Data ◽  
Large Scale ◽  
Weather Conditions ◽  
Spatial Data Analysis ◽  
Vegetation Coverage ◽  
Economic Activities ◽  
Traditional Methods ◽  
Detection Model ◽  
Nighttime Lights

Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

scholarly journals Remote Sensing of Vegetation Recovery in Grasslands after the 1988 Yellowstone Fires in Yellowstone National Park

1990 ◽  
Vol 14 ◽  
pp. 151-153
Author(s):  
Evelyn Merrill ◽  
Cathy Wilson ◽  
Ronald Marrs
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Satellite Imagery ◽  
Yellowstone National Park ◽  
National Park ◽  
Large Scale ◽  
Vegetation Recovery ◽  
Electromagnetic Spectrum ◽  
Traditional Methods ◽  
Vegetative Biomass

Traditional methods for measurement of vegetative biomass can be time-consuming and labor­intensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. One alternative to traditional methods for monitoring rangeland vegetation is to use satellite imagery. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, remote sensing of spectral data can be used to quantify the amount of green vegetative biomass present in an area (Tucker and Sellers 1986).

scholarly journals ESTIMATING THE AMOUNT OF SHIP RECYCLING ACTIVITY USING REMOTE SENSING APPLICATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1195-1200 ◽  
Author(s):  
M. Watagawa ◽  
T. Shinoda ◽  
K. Hasegawa
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Weather Conditions ◽  
Monitoring Methods ◽  
Economic Activities ◽  
Remote Sensing Application ◽  
Ship Breaking ◽  
Ship Recycling ◽  
Sensing Application ◽  
Sar Imagery

The Advanced Land Observing Satellite (ALOS) was launched for earth observation and there are more than 6 million scenes of archives including coastal areas during period of five years. The wealth of satellite imagery is noticeable for investigating monitoring methods such as ship detection in wide ocean area. Especially, it is useful way to estimate past behaviour from satellite imagery compared to reference data. We collected satellite imagery and analysis breaking process in major ship breaking yards between year 2009 and 2011. Comparing the number of recycling ships by satellite imagery to the world statistics is in good agreement. In this study, Remote Sensing Application has been discussed in order to assess the potential to be used for economic activities such as ship recycling in wide coastal area. It was used to evaluate the performance of ship recycling monitoring by Satellite imagery. Additionally, an approach for recognizing ships by SAR imagery regardless of weather conditions is presented.

Exploring regional and urban clusters and patterns in Europe using satellite observed lighting

2018 ◽  
Vol 47 (4) ◽  
pp. 553-568 ◽  
Author(s):  
Vassilis Tselios ◽  
Demetris Stathakis
Keyword(s):  
Spatial Data ◽  
Economic Activity ◽  
Urban Areas ◽  
Regional Analysis ◽  
Spatial Association ◽  
Cell System ◽  
Spatial Data Analysis ◽  
Urban Structure ◽  
Activity Levels ◽  
Nighttime Lights

We explore regional and urban clusters and patterns in Europe by using satellite images of nighttime lights and by employing Exploratory Spatial Data Analysis. We map Defense Meteorological Satellite Program nighttime lights data onto the nomenclature of territorial units for statistics III, Local Administrative Units II and pixel (i.e. 1 km2 grid cell system of Europe) level and apply global and local statistics of spatial association. Under the assumption that nighttime light data are a good proxy for economic activity, the analysis at regional level shows that the regions of global cities and megacities and their surrounding areas are hot spots of high economic activity levels. The regional analysis also reveals the polycentric hierarchical structure of Europe. Using the case studies of the regions of London and Île -de -France, the analysis at the urban level reveals the different urban structure of these two global regions and identifies the functional urban areas of London and Paris.

scholarly journals K-Prototypes Segmentation Analysis on Large-Scale Ridesourcing Trip Data

2020 ◽  
Vol 2674 (9) ◽  
pp. 383-394 ◽  
Author(s):  
Jason Soria ◽  
Ying Chen ◽  
Amanda Stathopoulos
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Weather Conditions ◽  
Data Types ◽  
Adverse Weather Conditions ◽  
Segmentation Analysis ◽  
Adverse Weather ◽  
Mobility System ◽  
Spatio Temporal

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.

scholarly journals A DISTRIBUTED POLYGON RETRIEVAL ALGORITHM USING MAPREDUCE

2015 ◽  
Vol II-4/W2 ◽  
pp. 51-53
Author(s):  
Q. Guo ◽  
B. Palanisamy ◽  
H. A. Karimi
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Large Scale ◽  
Programming Model ◽  
Processing Technique ◽  
Spatial Data Analysis ◽  
Retrieval Algorithm ◽  
Quad Tree ◽  
Parallel Data ◽  
Hadoop Cluster

The burst of large-scale spatial terrain data due to the proliferation of data acquisition devices like 3D laser scanners poses challenges to spatial data analysis and computation. Among many spatial analyses and computations, polygon retrieval is a fundamental operation which is often performed under real-time constraints. However, existing sequential algorithms fail to meet this demand for larger sizes of terrain data. Motivated by the MapReduce programming model, a well-adopted large-scale parallel data processing technique, we present a MapReduce-based polygon retrieval algorithm designed with the objective of reducing the IO and CPU loads of spatial data processing. By indexing the data based on a quad-tree approach, a significant amount of unneeded data is filtered in the filtering stage and it reduces the IO overhead. The indexed data also facilitates querying the relationship between the terrain data and query area in shorter time. The results of the experiments performed in our Hadoop cluster demonstrate that our algorithm performs significantly better than the existing distributed algorithms.

scholarly journals ESTIMATING THE AMOUNT OF SHIP RECYCLING ACTIVITY USING REMOTE SENSING APPLICATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1195-1200
Author(s):  
M. Watagawa ◽  
T. Shinoda ◽  
K. Hasegawa
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Weather Conditions ◽  
Monitoring Methods ◽  
Economic Activities ◽  
Remote Sensing Application ◽  
Ship Breaking ◽  
Ship Recycling ◽  
Sensing Application ◽  
Sar Imagery

The Advanced Land Observing Satellite (ALOS) was launched for earth observation and there are more than 6 million scenes of archives including coastal areas during period of five years. The wealth of satellite imagery is noticeable for investigating monitoring methods such as ship detection in wide ocean area. Especially, it is useful way to estimate past behaviour from satellite imagery compared to reference data. We collected satellite imagery and analysis breaking process in major ship breaking yards between year 2009 and 2011. Comparing the number of recycling ships by satellite imagery to the world statistics is in good agreement. In this study, Remote Sensing Application has been discussed in order to assess the potential to be used for economic activities such as ship recycling in wide coastal area. It was used to evaluate the performance of ship recycling monitoring by Satellite imagery. Additionally, an approach for recognizing ships by SAR imagery regardless of weather conditions is presented.

scholarly journals SPATIAL ASSOCIATION TO CHARACTERIZE THE CLIMATE TELECONNECTION PATTERNS IN ECUADOR BASED ON SATELLITE PRECIPITATION ESTIMATES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 101-106
Author(s):  
D. Ballari ◽  
L. Campozano ◽  
E. Samaniego ◽  
D. Orellana
Keyword(s):  
Spatial Patterns ◽  
Spatial Data ◽  
Large Scale ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Climate Indices ◽  
Moran's I ◽  
Local Climate ◽  
Teleconnection Patterns

Abstract. Climate teleconnections show remote and large-scale relationships between distant points on Earth. Their relations to precipitation are important to monitor and anticipate the anomalies that they can produce in the local climate, such as flood and drought events impacting agriculture, health, and hydropower generation. Climate teleconnections in relation to precipitation have been widely studied. Nevertheless, the spatial association of the teleconnection patterns (i.e. the spatial delineation of regions with teleconnections) has been unattended. Such spatial association allows to characterize how stable (heterogeneity/dependent and statistically significant) is the underlying spatial phenomena for a given pattern. Thus our objective was to characterize the spatial association of climate teleconnection patterns related to precipitation using an exploratory spatial data analysis approach. Global and local indicators of spatial association (Moran’s I and LISA) were used to detect spatial patterns of teleconnections based on TRMM satellite images and climate indices. Moran’s I depicted high positive spatial association for different climate indices, and LISA depicted two types of teleconnections patterns. The homogenous patterns were localized in the Coast and Amazonian regions, meanwhile the disperse patterns had a major presence in the Highlands. The results also showed some areas that, although with moderate to high teleconnection influences, had a random spatial patterns (i.e. non-significant spatial association). Other areas showed both teleconnections and significant spatial association, but with dispersed patterns. This pointed out the need to explore the local underlying features (topography, orientation, wind and micro-climates) that restrict (non-significant spatial association) or reaffirm (disperse patterns) the teleconnection patterns.

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