Integration Of Spatial Analysis Research Articles

Urban habitats and food insecurity: Lessons learned throughout a pandemic

BackgroundAn increasing amount of literature raises the issue of food deserts and urban heterogeneity in larger metropolitan cores throughout North America. Specific to Canadian cities, the disparity between access to health, education, and affordable food is of growing concern. Recently, these drivers seem to be significantly linked to the propagation of COVID-19. This paper explores the spatially-explicit dynamics of food deserts in Toronto, by integrating Geographic Information Systems and machine learning to understand the clusters of food deserts. The integration of spatial analysis with self-organizing maps (SOM) offers insights on the relation between neighborhoods, geodemographic profiles and urban characteristics, and whether one might expect consequences of food insecurity given COVID-19. MethodsThe paper starts out with developing a machine learning algorithm based on SOM to define meaningful clusters within the hedonic dataset. Further to this, an exploratory regression was built per cluster as to allow an exploratory spatial analysis to derive an explanatory framework for the key characteristics of socio-economic profiles within the Greater Toronto Area and impacts of SARS-CoV-2. ResultsThe findings suggest that there are clear spatial profiles within the urban core of Toronto in regards to food deserts, showing a direct relation between socioeconomic characteristics and the results on environmental injustice and livability. These profiles are strongly linked with the areas of COVID-19 occurrence, and share a very similar socio-demographic profile, particularly in regards to young and lower income families. ConclusionThere are several food deserts currently in Toronto, Ontario. The integration of policies that involve public health and spatial decision-support, particularly when linked to machine learning to aggregate characteristics of big data, establish a multi-functional understanding of the complexity of food security. This has a direct relation with diet, environment, and the opportunity to enhance subjective well-being in city cores.

Spatiotemporal Model to Quantify Stocks of Metal Cladding Products for a Prospective Circular Economy

The traditional linear economy (LE) approach based on a “take-make-dispose” plan that has been used in building activities over a long period has a significant impact on the environment. In the LE approach, the used materials are usually sent to landfills rather than recycled, resulting in resource depletion and excessive carbon emissions. A circular economy (CE) is expected to solve these environmental problems by promoting material “closed-loop systems”. This study was intended to quantify and analyse the global warming potential (GWP) values of specific metal roofing and cladding products to promote CE thinking. A spatiotemporal model integrated with the life cycle assessment (LCA) tool was used to quantify the GWP value of the steel products in the investigated buildings. The study analysed ten case buildings located in six different cities in New Zealand: Auckland, Wellington, Hamilton, Palmerston North, Tauranga, and Christchurch. The production stages (A1–A3), water processing (C3), disposal (C4), and recycle, reuse, and recovery stages (D) were the focus of the study in analysing the GWP values of the product’s life cycle. The study found that the production stages became the most significant emitters (approximately 99.67%) of the investigated steel products’ GWP values compared to other selected life cycle stages. However, when considering the recycling stages of the steel products, the GWP value was reduced up to 32%. Therefore, by implementing the recycling process, the amount of GWP can be reduced, consequently limiting the building activities’ environmental impacts. In addition, the integration of spatial analysis and LCA was found to have potential use and benefit in future urban mining and the development of the CE approach in the construction industry.

Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era.

The research of environmental geochemistry entered the big data era. Environmental big data is a kind of new method and thought, which brings both opportunities and challenges to GIS-based spatial analysis in geochemical studies. However, big data research in environmental geochemistry is still in its preliminary stage, and what practical problems can be solved still remain unclear. This short review paper briefly discusses the main problems and solutions of spatial analysis related to the big data in environmental geochemistry, with a focus on the development and applications of conventional GIS-based approaches as well as advanced spatial machine learning techniques. The topics discussed include probability distribution and data transformation, spatial structures and patterns, correlation and spatial relationships, data visualisation, spatial prediction, background and threshold, hot spots and spatial outliers as well as distinction of natural and anthropogenic factors. It is highlighted that the integration of spatial analysis on the GIS platform provides effective solutions to revealing the hidden spatial patterns and spatially varying relationships in environmental geochemistry, demonstrated by an example of cadmium concentrations in the topsoil of Northern Ireland through hot spot analysis. In the big data era, further studies should be more inclined to the integration and application of spatial machine learning techniques, as well as investigation on the temporal trends of environmental geochemical features.

Post-frontier governance up in smoke? Free-for-all frontier imaginations encourage illegal deforestation and appropriation of public lands in the Brazilian Amazon

ABSTRACT Post-frontier governance in the Brazilian Amazon highlights the interaction of regulatory technologies, institutions and practices to combat illegal deforestation and appropriation of public lands. While the Amazon fires received tremendous media coverage in 2019, the dismantlement of environmental laws and regulatory agencies has supported the rise in deforestation as early as 2012. We discuss the impact of post-frontier governance in Southwest-Pará by analyzing the spatial distribution of deforestation, territorial management, law enforcement, and environmental regularization from 2000 to 2018. The integration of spatial analysis with qualitative field surveys in the blacklisted municipality Novo Progresso shows that deforestation control largely fails, specifically in undesignated public lands. While land tenure security remains the biggest challenge in the region, the persistent imagination of free-for-all frontier spaces encourages the uncontrolled appropriation of and speculation with public lands. Here, the Rural Environmental Registry (CAR) has been identified as a key instrument to secure land claims.

Integration of spatial analysis and an agent-based model into evacuation management for shelter assignment and routing

Evacuation planning is a complex and challenging task as it requires an integrated approach to heterogeneous spatial datasets, including population, road network and facilities. Evacuation management of emergency situations is often based on delineation of evacuation circumstances and spatial analyses of the datasets. A practical evacuation management system would be a system that provides an easy configuration to evacuation managers who may not have full knowledge of spatial analysis functionality of geographic information system (GIS) software but need to understand the emergency situation promptly and provide decisive instructions which can be fulfilled when they manage the datasets and develop new workflows in various scenarios. An agent-based simulation approach is capable of demonstrating the spatio-temporal aspect of complex dynamic systems. Spatial analysis toolkits embedded in GIS software and open-source software are utilised in order to demonstrate the feasibility of enabling users to manage spatial data and customise the spatial analysis by combining common data analysis tools to meet their requirements in evacuation planning from both spatial and temporal perspectives. This paper proposes a shelter assignment and routing strategy for evacuating households at the 2011 Brisbane flood event as a scenario-based case study. The result shows that communities located in the east and west of Brisbane are scarcely covered by existing shelters. Furthermore, an agent-based simulation for households located in the service areas of three shelters indicates that the corresponding clearance time is 50 minutes. It is envisaged that this study can assist urban planners and emergency managers in developing an effective evacuation plan for a flood disaster in Brisbane.

Integration of spatial analysis and fuzzy classification for the estimation of forest parameters in Mediterranean areas

Satellite images are often insufficient to provide reliable estimates of forest parameters in complex Mediterranean areas, where the spectral response of vegetation is influenced by several interacting factors. In the current work spatial analysis and Geostatistics are used to produce additional information which can supplement that derived from remotely sensed data. In particular, an approach based on kriging was applied in a study area in Tuscany (Central Italy) to estimate forest composition and structure, and the same was done with a fuzzy classification procedure using bitemporal Landsat‐TM images. Since the two methods produced per‐pixel estimates of error variance, their outputs could be integrated by means of an optimal merging methodology. The results of the experiments, evaluated by statistical comparison to independent ground references, showed that both methods provided good estimates of forest composition and structure at stand level. Furthermore, the information derived from the two sources ...

Some thoughts on the integration of spatial analysis and Geographic Information Systems

It is past time for us to examine the relationship that exists between what we commonly call spatial analysis on the one hand, and geographic information systems or GIS on the other. GIS technology is widely used but seems to incorporate only a portion of the tools found in spatial analysis. Spatial analysis can be accused of overly simplistic approaches that severely limit its practical utility. This paper suggests that discarding myopic views and meeting upon a common ground of increased knowledge of modern computing concepts and techniques, including object-orientation, can prove extremely fruitful to both sides.

Ecological Time Series.

Part I: Analysis and methodology. Can ecological theory cross the land-sea interface? A modern view of applied time series analysis The great ocean conveyer Integration of spatial analysis in long-term ecological studies A general review of dynamical systems Analysis of long-time series Part II: Comparisons of scales. Physical and biological scales of variability in lakes, estuaries, and the coastal ocean Ecological variability at the land-sea interface Scales of variability in the Central Pacific Temporal variability in the North Atlantic Planning long-term vegetation studies at landscape scales Comparisons of ecological time series Part III: Processes and principles. The problem of pattern and scale in ecology Long-term environmental change Physical dynamics and ecological processes: The North Atlantic model Long-term trends in an Arctic ecosystem Epidemiological approaches in ecological modelling The invisible present Ecological processes in time series.

The integration of spatial analysis and gis

It is widely expected that future GISs will have increased analytical capabilities that will take them beyond being efficient display and database management devices. Several attempts have already been made to link existing analytical software to various GISs. However, a problem with all of these attempts is that the user is forced to switch back and forth between the GIS operating environment and the analytical software. In this paper we present a Statistical Analysis Module, SAM, that runs totally within the operating environment of a GIS and utilizes a command structure that makes running the package transparent to the user of the GIS. We believe that the development of this module is an important step in linking spatial analysis with GIS technology.