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Volume 2, Issue 3 (10-2015)
Spatial analysis as the main approach of geography was reviewed and searched through its historical development. The results of this exploratory research showed that this approach was born after the Second World War due to the overall interest of geographers to develop universal theories and laws. The advocators of this field believed that the old regional geography was not able to develop a scientific and applied knowledge. The main motivation of the development of the spatial analysis was the quantitative revolution of the 1960’s which was triggered by the article published by Shaeffer in 1953. This was followed by some prominent geographers such as Bungeh, Ulman, Barry, Hagget, Chorley and others. Bungeh and Harvey strengthened the philosophical foundation of spatial analysis and others such as Hagget , Chorley and Hajestrand published important books in the field of quantitative geography. The main objective of spatial analysis is to analyze the distributions through the identification of their global and local structures and reasoning these structures by their spatial relationship with other distributions. In this regard it uses quantitative data and mathematical language to achieve the spatial theories and laws.
The spatial analysis studies the spatial distributions and structures. These are the entities that are not subject to the human interpretation and thinking. This approach is true in the both physical and human geography. The knowledge it tries to achieve is the theories and laws about the spatial distributions. The methodology of spatial analysis is the quantitative methods such as experiment and survey. Thus in terms of ontology the entities of spatial analysis are independent of human mind and objective. The spatial characteristics of distributions are not constructed but discovered. The methodology used in spatial analysis is quantitative and objective including some methods such as experiment and survey. In 1980 and onward, human geography tried to move toward qualitative methods such hermeneutics but during 21st century all branches of geography are using quantitative methods more frequently than qualitative ones; but the use of the combined version of quantitative and qualitative methods is becoming more frequent day by day.
The introduction of Geographic Information System as the operational environment for spatial analysis works the approach has become more widespread and dominant. Geographers are now able to analyze more spatial data and discover more spatial theories to solve the spatial problems. GIS is the main tool for spatial analysis and by introducing the science of geostatistics has improved the scientific and applied power of spatial analysis. The application of quantitative geography including geostatistics and GIS requires improved knowledge of mathematics, geometry and statistics; the main language of today geography. The spatial analysis covers the important topics of geography including spatial distributions, regions, spatial relations especially the relation between human and environment, spatial structures, spatial reasoning, interpolation, and the most important topic of spatial planning. The spatial analysis is the only scientific field to define and develop spatial planning. With correct and logic spatial planning there won’t be any environmental hazards. Because in any region all human settlements and activities are planned according the potentials of the region.
Volume 8, Issue 4 (3-2022)
Spatial analysis of the factors influencing households’ direct energy
consumption and CO2 emission in Ardabil
Problem Statement
Carbon management and its production resources are important not only for the preservation of non-renewable resources but also for the prevention of global warming and its adverse consequences. Direct consumption of fuel and energy by households plays a major role in CO2 production and it’s spatial distribution. Therefore, in order to plan and manage carbon emissions, it is very important to identify the factors influencing household energy consumption. This paper aimed to investigate the relationship between household characteristics such as age, income, family size, household head age, house area, etc. and energy consumption which ordinally results in more emissions. The study area is Ardabil city. It has an area of 6289 ha and a population of about 530000 people.
Research Method
Consumption of natural gas, electricity and car fuel has been the criteria for determining the amount of household energy consumption. The data of the first two cases obtained from the bills of household’s consumption and the data of car fuel consumption and the other other required data, were collected through a survey as well. Based on the Cochran's formula, statistical samples including 383 households were selected as a sample of the households residing in Ardabil. A questionnaire was also used to collect the data. Data on energy consumption variables were first converted to Mj and then converted to CO2 emissions. The data was then entered into Arc GIS to draw spatial distribution maps using Kriging interpolation Tool. Finally, using TerrSet Geospatial Monitoring and Modeling System software, the spatial relationship maps were produced and the adjusted R values were calculated.
Findings and Conclusions
Findings demonstrate that in Ardabil, household fuel consumption cause to an emission of more than 226,515 grams of CO2 per household every month which is three times more than the mean value for all the Iranian households. In the study area, the average amount of energy consumption and carbon emission of households residing in municipality districts 2 and 3 are higher than same figure for all the households residing in the city. In contrast, in the municipality districts of 1 and 5, energy consumption and CO2 emission are lower than the mean value for the whole Ardabil households. In district 4, the figure is very close to the mean value for all the households. More than 80 percent of household CO2 emission emitted from fuel consumption in homes and this ratio is almost the same throughout the city and in all municipality districts. After that, the ratio of transportation CO2 emission is about 15%, and electricity consumption has a ratio of less than 5% as well. In four lots located in the southwest, north, northeast and the center of the city, every year, households emit less than 172640 g/m of CO2. In contrast, in 4.8% of the city surface area, the lots located in southwestern and southeastern, households’ emission of CO2 is the most (more than 308923 g/m). The adjusted R, which represents the spatial relationship between the variables with CO2 emission, for all the 11 variables, were 0.67, 0.66, 0.72, 0.80, 0.87 and 0.88 for the city, district 1, district 2, district 3, district 4 and district 5 respectively and these values indicate that there is a high correlation between these variables. The highest adjusted R values (0.8 and more) belong to the strip-shaped lots locate in the central and eastern fringes of the city and they cover almost half of the surface area of district 2 and a small part of district 1. Areas where R value is less than 0.2 cover almost the whole surface of district 5 in the northeast of the city. Also, variables of “number of people who have a driving license in any household”, “household head age”, “household size and “house surface area”, represent a high correlation between these variables and CO2 emissions. Also, the correlation between the variables level of “education of household head”, “household head income” and “having electrical appliances” indicate that there is the lowest correlation between the variables and with CO2 emissions.
Key Words: Energy, CO2, Household consumption, Spatial relation, Ardebil
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