scholarly journals Implementation of SExI–FS (Spatially Explicit Individual-based Forest Simulator) Model using UAV Aerial Photo Data Case Study: Jatinangor ITB Campus

2020 ◽  
Vol 27 (4) ◽  
pp. 314
Author(s):  
Aminah Kastuari ◽  
Deni Suwardhi ◽  
Himasari Hanan ◽  
Ketut Wikantika ◽  
Agung Budi Harto ◽  
...  
Keyword(s):  
Spatial Data ◽  
Landscape Architecture ◽  
Dynamic Changes ◽  
Dynamic Data ◽  
Vegetation Growth ◽  
Existing Structure ◽  
3D City Modelling ◽  
Initial Research ◽  
City Models

Landscape architecture affected by interaction between built and natural environment such as vegetation. Nowadays, landscape architects are using 3D city models for simulations, which requires highly dynamic and time-varying attributes. 3D city modelling structure has been standardized by CityGML, although researches that are related to the storing of dynamic data had been conducted for the past years, it has not been supported by any standard until this very moment. In dynamizer, it is added as a data structure into a CityGML structure that is already existed, although the existing structure is a static one. Kolbe’s research on dynamic data using CityGML called dynamizer could use the spatial data in more dynamic way by changing its geometric, thematic, or appearance data, but its purpose is not specific for trees or vegetation. In this paper, a method of simulating the vegetation growth using SeXI-FS will be discussed to show the dynamic changes that happen in vegetation as part of the dynamic changes in landscape architecture. The result of this research will be used to address the importance of information on vegetation by studying its changes in Jatinangor ITB Campus and as initial research to build dynamizer in CityGML for landscape architecture.

scholarly journals CITYGML APPLICATION DOMAIN EXTENSION FOR 3D STRATA REPRESENTATIONS IN THE SMARTKADASTER SYSTEM: TOWARDS BEYOND CADASTRE PURPOSE IN MALAYSIA

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 99-103
Author(s):  
C. B. Siew ◽  
N. Z. Abdul Halim ◽  
H. Karim ◽  
M. A. Mohd Zain ◽  
K. S. Looi
Keyword(s):  
Spatial Data ◽  
Application Domain ◽  
Kuala Lumpur ◽  
Levels Of Detail ◽  
Emerging Trends ◽  
Digital Twins ◽  
Domain Extension ◽  
Data Output ◽  
3D City Modelling ◽  
City Models

Abstract. Recent advancements in 3D city modelling and emerging trends in implementing and realising Digital Twins motivate the Department of Survey and Mapping Malaysia (JUPEM) to develop and implement SmartKADASTER (SKiP) Phase 2. SmartKADASTER Phase I was a precursor to this system, and it primarily focused on applying two-dimensional (2D) spatial data for 3D spatial analysis. CityGML was used as the data model for various Levels of Detail (LoD) in this new initiative to represent city models across the Greater Kuala Lumpur region. SmartKADASTER however, lacks strata information. Therefore, to integrate strata information into the SKiP citymodel environment, an Application Domain Extension (ADE) for CityGML has been developed to convert existing Strata XML to StrataGML, a CityGML-compliant data output format. This paper describes the purpose of the SmartKADASTER initiative in Section 1. Section 2 explains additional context for the initiative as well as some backgrounds. Section 3 discusses the conversion workflow and ADE definitions, followed by a brief discussion of visualisation in Section 4 and a project summary in Section 5.

Impact of the Quality of Spatial 3D City Models on Sensor Networks Placement Optimization

GEOMATICA ◽  
2012 ◽  
Vol 66 (4) ◽  
pp. 291-305 ◽  
Author(s):  
Meysam Argany ◽  
Mir Abolfazl Mostafavi ◽  
Vahab Akbarzadeh ◽  
Christian Gagné ◽  
Reda Yaagoubi
Keyword(s):  
Sensor Network ◽  
Urban Area ◽  
Spatial Data ◽  
Optimization Algorithms ◽  
Spatial Models ◽  
Spatial Data Quality ◽  
The Impact ◽  
City Models

Sensor networks are increasingly used for tracking, monitoring, and observing spatial dynamic phenomena in the real world (e.g. urban area). In order to ensure an efficient deployment of a sensor network, several optimization algorithms have been proposed in recent years. Most of these algorithms often rely on oversimplified sensor models. In addition, they do not consider information on the terrain topography, city models, and the presence of diverse obstacles in the sensing area (e.g. buildings, trees, poles). Only some of those optimization algorithms attempt to consider the terrain information in the optimization of a sensor network deployment. However, most of these algorithms consider that the spatial models used for this purpose are perfect representations of the reality and are not sensitive to the quality of the information. However, spatial models are simplified representations of a complex reality, and hence are inherently uncertain. In this paper we will investigate the impact of the spatial data quality on the optimization of a sensor network and its spatial coverage in an urban area. For this purpose, we will investigate specific implications of spatial data quality criteria for a 3D city model that will be used in sensor network optimization algorithms. Then, we will analyze the impact of some of those criteria on the estimation of sensor network coverage.!Afterwards, a case study for sensor network deployment in an urban area will be presented. This case study will demonstrate the impact of 3D city models quality on the estimation of coverage using global and local optimization algorithms. Finally, the results obtained from this experimentation will be presented and discussed.

scholarly journals Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach

Solar Energy ◽  
2017 ◽  
Vol 146 ◽  
pp. 264-275 ◽  
Author(s):  
Laura Romero Rodríguez ◽  
Eric Duminil ◽  
José Sánchez Ramos ◽  
Ursula Eicker
Keyword(s):  
Methodological Approach ◽  
3D City Models ◽  
Urban Level ◽  
City Models

Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina

2021 ◽  
pp. 135481662098768
Author(s):  
Laura I Luna
Keyword(s):  
Decision Making ◽  
Principal Components ◽  
Spatial Data ◽  
Local Development ◽  
Value Added ◽  
Tourism Industry ◽  
Methodological Innovation ◽  
Spatial Principal Components ◽  
Components Analysis

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

Education as the Medium of Upward Mobility: The Case of Dooms of Jammu

2021 ◽  
pp. 097318492110070
Author(s):  
Amithy Jasrotia ◽  
Smriti Srivastava
Keyword(s):  
First Generation ◽  
Upward Mobility ◽  
Educational Institutions ◽  
Process Of Change ◽  
Detailed Case ◽  
Existing Structure ◽  
Course Of Study

The current study explores the multifaceted and entwined structure of constraints and spaces of the possibilities of moving ahead among the Dooms of Jammu, India, where the possibilities of upward mobility through education as a means have been observed. Interviews and detailed case study were done with eight cases. Four overlapping super-ordinate themes developed during the course of study: (a) challenges of different generation learners, (b) lack of different forms of capital, (c) dis-identification from own and emulating others and (d) mushrooming of hybrid and mimic generation. The participants experienced the very process of change and continuity through education in their lives. It is observed that education helped in converting the morphology of their existing structure. Each of the interviewee has some exclusive experiences to share, offering significant insights into their lives, struggles and their conditions. The results indicate that the first-generation learners have to face many obstacles. The study concludes that education gives better results under certain circumstances. The chances of low caste children performing better are higher if the educational institutions run with mixed batches with students belonging to all the sections of the society.

scholarly journals Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia

2021 ◽  
Author(s):  
Emmanuel Skoufias ◽  
Eric Strobl ◽  
Thomas Tveit
Keyword(s):  
Spatial Data ◽  
Volcanic Ash ◽  
Satellite Images ◽  
Fragility Curves ◽  
Volcanic Eruptions ◽  
Damage Indices ◽  
Damage Indicator ◽  
Time Period ◽  
Peak Ground Motion

AbstractThis article demonstrates the construction of earthquake and volcano damage indices using publicly available remote sensing sources and data on the physical characteristics of events. For earthquakes we use peak ground motion maps in conjunction with building type fragility curves to construct a local damage indicator. For volcanoes we employ volcanic ash data as a proxy for local damages. Both indices are then spatially aggregated by taking local economic exposure into account by assessing nightlight intensity derived from satellite images. We demonstrate the use of these indices with a case study of Indonesia, a country frequently exposed to earthquakes and volcanic eruptions. The results show that the indices capture the areas with the highest damage, and we provide overviews of the modeled aggregated damage for all provinces and districts in Indonesia for the time period 2004 to 2014. The indices were constructed using a combination of software programs—ArcGIS/Python, Matlab, and Stata. We also outline what potential freeware alternatives exist. Finally, for each index we highlight the assumptions and limitations that a potential practitioner needs to be aware of.

scholarly journals A Weighted-Time-Lag Method to Detect Lag Vegetation Response to Climate Variation: A Case Study in Loess Plateau, China, 1982–2013

2021 ◽  
Vol 13 (5) ◽  
pp. 923
Author(s):  
Qianqian Sun ◽  
Chao Liu ◽  
Tianyang Chen ◽  
Anbing Zhang
Keyword(s):  
Climate Change ◽  
Loess Plateau ◽  
Time Lag ◽  
New Method ◽  
Climate Factors ◽  
The Loess Plateau ◽  
Climate Conditions ◽  
Vegetation Growth ◽  
Lag Effect

Vegetation fluctuation is sensitive to climate change, and this response exhibits a time lag. Traditionally, scholars estimated this lag effect by considering the immediate prior lag (e.g., where vegetation in the current month is impacted by the climate in a certain prior month) or the lag accumulation (e.g., where vegetation in the current month is impacted by the last several months). The essence of these two methods is that vegetation growth is impacted by climate conditions in the prior period or several consecutive previous periods, which fails to consider the different impacts coming from each of those prior periods. Therefore, this study proposed a new approach, the weighted time-lag method, in detecting the lag effect of climate conditions coming from different prior periods. Essentially, the new method is a generalized extension of the lag-accumulation method. However, the new method detects how many prior periods need to be considered and, most importantly, the differentiated climate impact on vegetation growth in each of the determined prior periods. We tested the performance of the new method in the Loess Plateau by comparing various lag detection methods by using the linear model between the climate factors and the normalized difference vegetation index (NDVI). The case study confirmed four main findings: (1) the response of vegetation growth exhibits time lag to both precipitation and temperature; (2) there are apparent differences in the time lag effect detected by various methods, but the weighted time-lag method produced the highest determination coefficient (R2) in the linear model and provided the most specific lag pattern over the determined prior periods; (3) the vegetation growth is most sensitive to climate factors in the current month and the last month in the Loess Plateau but reflects a varied of responses to other prior months; and (4) the impact of temperature on vegetation growth is higher than that of precipitation. The new method provides a much more precise detection of the lag effect of climate change on vegetation growth and makes a smart decision about soil conservation and ecological restoration after severe climate events, such as long-lasting drought or flooding.

A Framework of Local Geospatial Data Infrastructure for Sustainable Urban Development

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Azman Ariffin ◽  
Nabila Ibrahim ◽  
Ghazali Desa ◽  
Uznir Ujang ◽  
Hishamuddin Mohd Ali ◽  
...  
Keyword(s):  
Urban Development ◽  
Spatial Data ◽  
Geospatial Data ◽  
Sustainable Urban Development ◽  
Data Infrastructure ◽  
Shape Category ◽  
Key Dimensions ◽  
Data Layers ◽  
Conceptual Data

This paper addresses the need to develop a Local Geospatial Data Infrastructure (LGDI) for sustainable urban development. This research will highlight the effective and efficient framework for the development of local infrastructure. This paper presents a framework (a combination of domain based and goal based frameworks) for developing a Local Geospatial Data Infrastructure. The basis of this research is on a case study conducted in a Malaysian city. The main focus of the case study was on measuring and assessing sustainability. Six conceptual frameworks were produced based on 6 key dimensions of sustainability. The developed framework consists of 6 conceptual data models and 6 conceptual data structures. It was concluded that 30 spatial data layers are needed of which 12 data layers are categorized as point shape, 17 data layers are categorized as polygon shape and 1 data layer as line shape category.

scholarly journals Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Carlos A. Felgueiras ◽  
Jussara O. Ortiz ◽  
Eduardo C. G. Camargo ◽  
Laércio M. Namikawa ◽  
Thales S. Körting
Keyword(s):  
Spatial Data ◽  
Three Dimensional ◽  
Real Data ◽  
Geographic Region ◽  
Geostatistical Modeling ◽  
Probability Maps ◽  
Uncertainty Model ◽  
Probability Criterion ◽  
Sample Points

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.

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