Neighborhood relation diagrams for local comparison of carbon footprints in urban planning

2012 ◽  
Vol 11 (2) ◽  
pp. 124-135 ◽  
Author(s):  
Daniel Engel ◽  
Sebastian Petsch ◽  
Hans Hagen ◽  
Subhrajit Guhathakurta
Keyword(s):  
Urban Planning ◽  
Carbon Footprint ◽  
Spatial Data ◽  
Spatial Information ◽  
Spatial Location ◽  
Carbon Footprints ◽  
Local Comparison ◽  
Neighborhood Relation ◽  
Constrained Deformation ◽  
New Research

The dreaded effects of climate change have led to a new research focus in many applications. In urban planning, the visualization of carbon footprints has become one of the most sought after aspects. Urban planning data of carbon footprints contains spatial (location) and abstract (statistical indicators) information. Although many techniques for the visualization of such partially spatial data have been successfully applied in the area of geovisualization, the core focus has been on a global depiction of non-spatial information. However, conducting local comparisons, as in the case of comparing neighborhood districts and households, is of particular importance in investigative tasks. Additionally, representing different carbon footprint indicators (multiple non-spatial parameters) and unstructured parameter values (resulting in scaling issues) in a static representation provides an interesting challenge for visualization. This paper describes a novel and generic solution to the above-mentioned issues: a neighborhood relation diagram for the local comparison of non-spatial information in partial spatial data. The technique is based on the geometric computation of Voronoi diagrams according to a weighted neighborhood metric. The shape of spatial regions (e.g. city districts) within this diagram is characterized by a directed and constrained deformation according to the non-spatial (i.e. carbon footprint) relations to neighboring regions. The effectiveness of our method is highlighted in a preliminary study of carbon footprint patterns in downtown Phoenix (Arizona, USA). In this study, neighborhood relation diagrams enable city planners to detect local effects on carbon emissions and their relation to planning projects.

THE NEXT GENERATION SPATIAL DATA WAREHOUSE

2002 ◽  
Vol 42 (1) ◽  
pp. 633
Author(s):  
A.J. Yardley
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Essential Element ◽  
Spatial Location ◽  
Geographic Information ◽  
Environmental Data ◽  
Next Generation ◽  
Data Types ◽  
Political Boundaries ◽  
Access To Data

Woodside Energy, based in Perth, Western Australia, has commenced the implementation of its next generation spatial data warehousing and visualisation system. The warehouse facilitates access to data in various corporate geoscience data sets, as well as up-to-date cultural and environmental data. It expands the capabilities of the existing geoscience database by providing a facility to handle spatial data at the database level rather than in files and maps. Spatial data can now be kept in the database, in its correct spatial location, and with a known provenance.Woodside’s worldwide exploration, development and production activities require the use of a wide variety of geographic data such as seismic, bathymetry, wells, permits, coastlines, political boundaries, navigation charts, remote sensing and geological interpretations.Geo-spatial data comes to Woodside in a variety of formats, datums and conditions. The Geomatics Department, through the Geoscience Database and Spatial Information Management teams, loads, maintains and manages all data considered to be corporate. It is quality controlled and placed into the warehouse, where it is readily accessible to technical and administrative staff.Location is an essential element in most Woodside decisions. Because of the new spatial capabilities, a number of geographic information processes are now possible. Additionally information can also be made available through the internet if required.Reliable geographic information will become more widely available in the organisation, and be more easily merged with traditional data types, enhancing the decision-making process.

Design and Realization of Digital Urban Planning Spatial Database

2013 ◽  
Vol 756-759 ◽  
pp. 1824-1827 ◽  
Author(s):  
Ze Yun Yang ◽  
Jin Ling Yang ◽  
Xian Ge Cao ◽  
Xiu Hai Li ◽  
Xin Liang ◽  
...  
Keyword(s):  
Urban Planning ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Extraction ◽  
Spatial Database ◽  
Spatial Query ◽  
Huge Amount ◽  
Gis Application ◽  
Attribute Data ◽  
Digital Urban Planning

Based on studying Digital Urban Planning spatial database, this subject uses spatial data engine ArcSDE as interface between GIS application server and database server, and takes the ArcSDE as the core to realize spatial query and spatial analysis of digital urban planning spatial information, and then unified managed the spatial data and attribute data of digital urban planning, finally to support efficient, the huge amount of data extraction.

Implementation of Urban 3D Geographic Information System

2014 ◽  
Vol 926-930 ◽  
pp. 721-724
Author(s):  
Zhao Zhong Gao ◽  
Hai Xia Wei
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Urban Planning ◽  
Information Management ◽  
Spatial Data ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geographic Information ◽  
Urban Construction ◽  
Depth Analysis

With the digital development of city construction, the construction of three-dimensional Geographic Information System plays an important role for the urban construction planning and decision-making. 3D urban planning geographic information management systems need to be able to put different spatial data, information of urban construction, urban planning information into the same platform. The integration of information resources whick provids a variety of spatial information based on the intelligent application services is the core. This article puts urban planning geographic information management related to business needs in-depth analysis, and put forward a three-dimensional geographic information model which is used for integrated management of data and can be dynamically adjusted for urban planning and management of business processes.

DARCY RIBEIRO UNIVERSITY CAMPUS MAP – 2017- TECHNICAL NOTE

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Rafael Sanzio Araújo dos Anjos ◽  
Jose Leandro de Araujo Conceição ◽  
Jõao Emanuel ◽  
Matheus Nunes
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Technical Note ◽  
University Campus ◽  
Communication Tool ◽  
Sources Of Information ◽  
Data Manipulation ◽  
Geographic Space ◽  
The Many ◽  
New Generation

The spatial information regarding the use of territory is one of the many strategies used to answer and to inform about what happened, what is happening and what may happen in geographic space. Therefore, the mapping of land use as a communication tool for the spatial data made significant progress in improving sources of information, especially over the last few decades, with new generation remote sensing products for data manipulation.

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

EXPRESS: Sustainable by Design: Choice Architecture and the Carbon Footprint of Grocery Shopping

2021 ◽  
pp. 074391562110088
Author(s):  
Luca Panzone ◽  
Alistair Ulph ◽  
Denis Hilton ◽  
Ilse Gortemaker ◽  
Ibrahim Tajudeen
Keyword(s):  
Carbon Footprint ◽  
Carbon Tax ◽  
Food Products ◽  
Grocery Shopping ◽  
Choice Architecture ◽  
Low Carbon ◽  
Policy Objective ◽  
Carbon Footprints ◽  
Design Choice ◽  
Goal Priming

The increase in global temperatures requires substantial reductions in the greenhouse emissions from consumer choices. We use an experimental incentive-compatible online supermarket to analyse the effect of a carbon-based choice architecture, which presents commodities to customers in high, medium and low carbon footprint groups, in reducing the carbon footprints of grocery baskets. We relate this choice architecture to two other policy interventions: a bonus-malus carbon tax on all grocery products; and moral goal priming, using an online banner noting the moral importance of reducing one’s carbon footprint. Participants shopped from their home in an online store containing 612 existing food products and 39 existing non-food products for which we had data on carbon footprint, over three successive weeks, with the interventions occurring in the second and third weeks. Choice architecture reduced carbon footprint significantly in the third week by reducing the proportion of choices made in the high-carbon aisle. The carbon tax reduced carbon footprint in both weeks, primarily by reducing overall spend. The goal priming banner led to a small reduction in carbon footprint in the second week only. Thus, the design of the marketplace plays an important role in achieving the policy objective of reducing greenhouse gas emissions.

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

scholarly journals Translating a Global Emission-Reduction Framework for Subnational Climate Action: A Case Study from the State of Georgia

2021 ◽  
Vol 67 (2) ◽  
pp. 205-227
Author(s):  
Marilyn A. Brown ◽  
Blair Beasley ◽  
Fikret Atalay ◽  
Kim M. Cobb ◽  
Puneet Dwiveldi ◽  
...  
Keyword(s):  
Carbon Footprint ◽  
Global Solutions ◽  
High Impact ◽  
Environmental Benefits ◽  
Future Research ◽  
Carbon Footprints ◽  
Carbon Reduction ◽  
Global Emission ◽  
Greenhouse Gas Reduction ◽  
Climate Action

AbstractSubnational entities are recognizing the need to systematically examine options for reducing their carbon footprints. However, few robust and comprehensive analyses are available that lay out how US states and regions can most effectively contribute. This paper describes an approach developed for Georgia—a state in the southeastern United States called “Drawdown Georgia”, our research involves (1) understanding Georgia’s baseline carbon footprint and trends, (2) identifying the universe of Georgia-specific carbon-reduction solutions that could be impactful by 2030, (3) estimating the greenhouse gas reduction potential of these high-impact 2030 solutions for Georgia, and (4) estimating associated costs and benefits while also considering how the solutions might impact societal priorities, such as economic development opportunities, public health, environmental benefits, and equity. We began by examining the global solutions identified by Project Drawdown. The resulting 20 high-impact 2030 solutions provide a strategy for reducing Georgia’s carbon footprint in the next decade using market-ready technologies and practices and including negative emission solutions. This paper describes our systematic and replicable process and ends with a discussion of its strengths, weaknesses, and planned future research.

scholarly journals Multi-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?

2021 ◽  
Vol 13 (2) ◽  
pp. 748
Author(s):  
Iana Rufino ◽  
Slobodan Djordjević ◽  
Higor Costa de Brito ◽  
Priscila Barros Ramalho Alves
Keyword(s):  
Land Use ◽  
Urban Planning ◽  
Cellular Automata ◽  
Land Use Change ◽  
Spatial Pattern ◽  
Urban Growth ◽  
Spatial Information ◽  
Dynamic Modelling ◽  
Urban Models ◽  
Extreme Hazards

The northeastern Brazilian region has been vulnerable to hydrometeorological extremes, especially droughts, for centuries. A combination of natural climate variability (most of the area is semi-arid) and water governance problems increases extreme events’ impacts, especially in urban areas. Spatial analysis and visualisation of possible land-use change (LUC) zones and trends (urban growth vectors) can be useful for planning actions or decision-making policies for sustainable development. The Global Human Settlement Layer (GHSL) produces global spatial information, evidence-based analytics, and knowledge describing Earth’s human presence. In this work, the GHSL built-up grids for selected Brazilian cities were used to generate urban models using GIS (geographic information system) technologies and cellular automata for spatial pattern simulations of urban growth. In this work, six Brazilian cities were selected to generate urban models using GIS technologies and cellular automata for spatial pattern simulations of urban sprawl. The main goal was to provide predictive scenarios for water management (including simulations) and urban planning in a region highly susceptible to extreme hazards, such as floods and droughts. The northeastern Brazilian cities’ analysis raises more significant challenges because of the lack of land-use change field data. Findings and conclusions show the potential of dynamic modelling to predict scenarios and support water sensitive urban planning, increasing cities’ coping capacity for extreme hazards.

The 3rd ACM SIGSPATIAL International Workshop on Geospatial Simulation

2021 ◽  
Vol 12 (3) ◽  
pp. 11-14
Author(s):  
Joon-Seok Kim ◽  
Taylor Anderson ◽  
Ashwin Shashidharan ◽  
Andreas Züfle
Keyword(s):  
Spatial Data ◽  
International Workshop ◽  
Data Sources ◽  
Relative Location ◽  
Technological Changes ◽  
Research Opportunities ◽  
New Methodologies ◽  
New Research

Space has long been acknowledged by researchers as a fundamental constraint which shapes our world. As technological changes have transformed the very concept of distance, the relative location and connectivity of geospatial phenomena have remained stubbornly significant in how systems function. At the same time, however, technology has advanced the science of geospatial simulation to bear on our understanding of how such systems work. While previous generations of scientists and practitioners were unable to gather spatial data or to incorporate it into models at any meaningful scale, new methodologies and data sources are becoming increasingly available to researchers, developers, users, and practitioners. These developments present new research opportunities for geospatial simulation.

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