Effectively learning spatial indices

2020 ◽  
Vol 13 (12) ◽  
pp. 2341-2354
Author(s):  
Jianzhong Qi ◽  
Guanli Liu ◽  
Christian S. Jensen ◽  
Lars Kulik
Keyword(s):  
Spatial Indices

scholarly journals Global and local spatial indices of urban segregation

2007 ◽  
Vol 21 (3) ◽  
pp. 299-323 ◽  
Author(s):  
F. F. Feitosa ◽  
G. Câmara ◽  
A. M. V. Monteiro ◽  
T. Koschitzki ◽  
M. P. S. Silva
Keyword(s):  
Spatial Indices ◽  
Global And Local

scholarly journals A Wind Tunnel Study on the Correlation between Urban Space Quantification and Pedestrian–Level Ventilation

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 564 ◽  
Author(s):  
Li ◽  
Peng ◽  
Ji ◽  
Hu ◽  
Ding
Keyword(s):  
Wind Speed ◽  
Wind Tunnel ◽  
Wind Velocity ◽  
Urban Space ◽  
Speed Ratio ◽  
Spatial Indices ◽  
Spatial Characteristics ◽  
Spatial Partition ◽  
Mean Wind Speed ◽  
Partition Method

Correlation research on urban space and pedestrian–level wind (PLW) environments is helpful for improving the wind comfort in complex urban space. It could also be significant for building and urban design. Correlation research is usually carried out in a space with clear urban spatial characteristics, so it is necessary to define the space first. In this paper, a typical urban area in Nanjing, China, is selected as the research object, and a spatial partition method is used to divide the real complex urban space into subspaces. The urban spatial characteristics of such subspaces are quantified using three urban spatial indices: openness (O), area (A), and shape (S). By comparing the quantitative results, 24 (12 pairs) subspaces with prominent urban spatial indices are selected as the correlation research cases. The 24 subspaces also provide a reference for the layout of the measurement points in a wind tunnel experiment. This is a new arrangement for locating the measurement points of a wind tunnel for correlation research. In the experiment, 45 measurement points are located, and the mean wind velocity of four different wind directions at 45 measurement points is experimented. The results clearly show that, when the experimental conditions are the same, the changes of mean wind velocity ratio (UR) of 24 (12pairs) subspaces under the four experimental wind directions are close. The URs of the subspaces are not significantly affected by the wind direction, which is affected more by the subspaces’ spatial characteristics. When making the correlation analysis between mean wind speed ratio and spatial characteristics’ indices, a direct numerical comparison was not able to find a correlation. By comparing the difference values of mean wind speed (△UR) and indices between each pair of subspaces, the correlation between UR and openness of subspaces were found. Limited by spatial partition method, the correlation between UR and the other indices was not obvious.

A Performance Evaluation of Spatial Indices for Geospatial Publish/Subscribe Systems

2019 ◽  
Author(s):  
Kresimir Pripuzic ◽  
Damjan Katusic ◽  
Martina Marjanovic ◽  
Aleksandar Antonic ◽  
Ivan Livaja
Keyword(s):  
Performance Evaluation ◽  
Spatial Indices ◽  
A Performance

scholarly journals Proximity and Self-Correlation of Municipalities in the Republic of Benin

2019 ◽  
Vol 10 (1) ◽  
pp. 125
Author(s):  
ACACHA Hortensia
Keyword(s):  
Local Development ◽  
Methodological Approach ◽  
Capital Expenditures ◽  
Tax Revenue ◽  
Spatial Indices ◽  
Tax Revenues ◽  
The Commons ◽  
Republic Of Benin ◽  
The Republic ◽  
Investment Spending

The purpose of this study is to provide a detailed description of the spatial distribution of tax revenues, non-tax revenues and capital expenditures of the Communes in the Republic of Benin, to analyze the spatial interactions between the Commons and to deduce from them Communes with a strong neighborhood of spatial interaction.The methodological approach consisted in using a database, in the construction of an adjacency matrix that made it possible to conceptualize and take into account the neighborhood links. The degree of spatial dependence is captured from the global and local spatial indices of Moran.The main findings of the study indicate that the tax revenues and capital expenditures of the Commons are characterized by a random distribution. On the other hand, non-tax revenue is spatially self-correlated. However, local spatial analyzes reveal that some municipalities seem to be concentrating above average levels of tax revenue and investment spending in their neighborhoods. In addition, the analyzes revealed that the influence of the urbanization rate on the level of tax revenue, non-tax revenue and investment expenditure is barely perceptible. It is therefore necessary to review a better orientation of local development policies.

Quantitative Analysis Indices of Highway Landscape Design

2015 ◽  
Vol 744-746 ◽  
pp. 1283-1287
Author(s):  
Huai Zhi Zhou ◽  
Dong Xuan Wei ◽  
Hong Zhi Yang
Keyword(s):  
Visual Acuity ◽  
Quantitative Analysis ◽  
Index System ◽  
Diversity Indices ◽  
Landscape Design ◽  
Sensitivity Index ◽  
Spatial Indices ◽  
Sensitivity Indices ◽  
Landscape Sensitivity ◽  
Set Up

In this paper, the problems existing in highway landscape design is studied on. The quantitative analysis index system of highway landscape design is set up, which is made up of diversity indices, spatial indices, sensitivity indices. The method of determining these indices is analyzed. In sensitivity indices, landscape sensitivity index and visual acuity index are set up based on the dynamic and static characters of highway landscape.

Tactical Predictions of Shoreline Oiling Probability via Machine Learning Models and Satellite-Derived Surface Oil Analysis Products

2014 ◽  
Vol 2014 (1) ◽  
pp. 660-672
Author(s):  
Zachary Nixon
Keyword(s):  
Machine Learning ◽  
Spatial Scales ◽  
Information Service ◽  
Habitat Type ◽  
Model Performance ◽  
Boosted Regression Trees ◽  
Oil Analysis ◽  
Spatial Indices ◽  
Spatial And Temporal Scales ◽  
Trajectory Models

ABSTRACT For significant oil spills in remote areas with complex shoreline geometry, apportioning Shoreline Cleanup Assessment Technique (SCAT) survey effort is a complicated and difficult task. Aerial surveys are often used to select shoreline areas for ground survey after an initial prioritization based upon anecdotal reports or trajectory models, but aerial observers may have difficulty locating cryptic surface shoreline oiling in vegetated or other complex environments. In dynamic beach environments, stranded shoreline oiling may be rapidly buried, making aerial observation difficult. A machine learning-based model is presented for estimating shoreline oiling probabilities via satellite-derived surface oil analysis products, wind summary data, and shoreline habitat type and geometry data. These inputs are increasingly available at spatial and temporal scales sufficient for tactical use, enabling model predictions to be generated within hours after satellite remote sensing products are available. The model was constructed using SCAT data from the Deepwater Horizon oil spill, satellite-derived surface oil analysis products generated during the spill by NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity, and available shoreline geometry, character, and other preexisting data. The model involves the generation of set of spatial indices of relative over-water proximity of surface oil slicks based upon the satellite-derived analysis products. The model then uses boosted regression trees (BRT), a flexible and relatively recently developed modeling methodology, to generate calibrated estimates of probability of subsequent shoreline oiling based upon these indices, wind climatological data over the time period of interest, and other shoreline data. The model can be implemented via data preparation in any Geographic Information System (GIS) software coupled with the open-source statistical computing language, R. The model is entirely probabilistic and makes no attempt to reproduce the physics of oil moving through the environment, as do trajectory models. It is best used in concert with such models to make estimates at different spatial scales, or when time and data requirements make implementation of fine-scale trajectory modeling impractical for tactical use. The details of model development implementation and assessments of model performance and limitations are presented.

Sign in / Sign up

Export Citation Format

Share Document