Zahra Bahari Sojahrood; Mohammad Taleai
Abstract
Extended Abstract
Introduction
The most important challenge in urban land use planning is the spatial organization of urban activities and functions based on the needs of urban society. Extracting the current rules that exist in the city not only adds local conditions to the standard values mentioned ...
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Extended Abstract
Introduction
The most important challenge in urban land use planning is the spatial organization of urban activities and functions based on the needs of urban society. Extracting the current rules that exist in the city not only adds local conditions to the standard values mentioned in various instructions (Habib et al, 1999; Shiea 2018; Saeedinia 2004) but also makes it possible to analyze with comparing the existing conditions of the city with the standards. There is some research to examine the current situation of the city. Most of these studies have used statistical methods (Hosseinzadeh et al. 1399; Omidipour et al, 2017; Mohammadnejad et al. 2012).
A few of them have utilized data mining methods, but none of these studies examine existing patterns between one type of land use with other land uses. In addition, the method used in this research is a new method that tries to use the capabilities of association rules and decision trees in exploring co-located patterns by combining these methods.Therefore, considering the importance and necessity of addressing this issue, the purpose of this research is to explore the current situation of urban land use by using data mining methods to discover the current patterns in the location of land uses in the vicinity and at different distances.
Finally, providing rules derived from these models may help planners and managers to understand the current status of land use appropriately and improve urban land-use plans by utilizing them in combination with standards and rules based on expert knowledge.
Materials & Methods
Spatial association rules
Association rules discover the laws of interdependence between the data of a large database. In other words, patterns that are frequently repeated in the data set are identified and used to explain the rules of dependence (Han & et al, 2011: 54; Li 2015). The rules of the association in which one of the propositions in the premise or sequence contains a spatial relation are called spatial association rules (Geissen & et al, 2007: 277-287, Mennis & et al, 2005: 5-17).
Decision Tree
The decision tree is one of the most powerful and common techniques for classification and prediction. Among the algorithms used to construct the decision tree, the most important is the C5 algorithm which is the developed ID3 algorithm.
Methodology
A n*l transaction matrix is generated. Where n is the number of available features and l represents the number of types of land use studied, which is 19 in this article. The elements of this matrix can be zero or one.
To fill the transaction matrix, we first consider the distance and apply buffer analysis for all the features in the land use layer. Then, for each feature, we intersect the buffer layer of that feature with the land-use layer and extract all the features that appeared at the intersection. Arc GIS software was used to perform spatial analysis.
Then, to extract the current rules of land use in the urban environment, the a priori algorithm is selected as one of the association rules algorithms, and the C5 algorithm is selected as one of the decision tree algorithms.
In this research, the user data of neighborhood 4, district 5 of Tehran Municipality, including 1065 property plots, were used.
Results & Discussion
In this step, the proposed model for deriving the rules of land use dependence based on the current situation of land use in the study area is implemented step by step and the results are presented.
According to existing standards, three distances are considered to extract spatial rules with an apriori algorithm. After extracting the rules, they are compared with the values of approved standards in urban land use planning. Vicinity and compatibility are examples of indicators in common standards for locating and determining land use for the land. Using the extracted rules, the indicators are examined.
Due to the lack of extraction of some rules by association rules, for example, not extracted rules related to therapeutic land uses within 300 meters from residential land uses, we use the decision tree algorithm to extract related rules in more detail. The graphs obtain from the decision tree shows which land uses are effective for predicting and categorizing specific land uses, based on the current status of the land uses located in the case study area.
Conclusion
The purpose of this paper is to data mining the current status of urban land uses to extract the rules of neighborhood and proximity of different land uses. Using the proposed model in this article, it is possible to extract the existing rules of land uses in detail and as well as to evaluate its compliance with conventional standards and criteria in urban land use planning.
Mehrdad AhangarCani; Seyyed Hossien Khasteh
Abstract
Extended Abstract Introduction and Objective Due to the location of Iran in dry regions of the Middle East, and also because of the rapid increase in its urban population and water consumption, every day the issue of water scarcity becomes more severe in Iran. In recent years, Iran has faced serious ...
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Extended Abstract Introduction and Objective Due to the location of Iran in dry regions of the Middle East, and also because of the rapid increase in its urban population and water consumption, every day the issue of water scarcity becomes more severe in Iran. In recent years, Iran has faced serious water scarcity and excessive consumption of water resources. Therefore, patterns of urban water consumption, different geographic, spatial, demographic, social, and economic parameters, and the relation between these parameter and water consumption are considered to be among important issues affecting management of water resources. The present study seeks to investigate and analyze the spatial pattern of domestic water consumption in Babol County, and also to identify parameters affecting the pattern of water use. This is achieved by extracting association rules from some spatial and socio-economic parameters and based on the water use level in this County. The study also aims to determine regions with high/low level of water use, investigate spatial distribution of water consumption and finally, identify and categorize parameters affecting domestic water consumption at neighborhood level in this County using Decision Tree model. Materials and methods Data: Domestic water consumption data, census data, spatial and socio-economic parameters such as distance from main roads, distance from Babolrood, total area of garden and green space in each building, building site and standing property (total area of house yard), population density, total number of houses vs. apartments, number of housing units, average number of people per household, percentage of young/old people per household were extracted from the Statistical Center of Iran for the time period of 2011 to 2016. Then, these data were used to analyze urban water consumption in Babol County. Methods: Apriori algorithm - a data mining algorithm used to extract association rules- has been used to discover and extract relationships between different spatial socio-economic parameters and domestic water consumption patterns. Moreover, a decision tree has been developed which takes advantage of these parameters to predict domestic water use. Results and Discussion Results indicated that number of houses, number of household members, green space in each house, total area of house yard and distance from main roads are directly related with the household water consumption. On the other hand, population density, percentage of youth population, number of residential units and distance from Babolrood River are inversely related to domestic water consumption. Among all parameters considered in the present study, total area of house yard, distance from Babolrood River, number of residential units and number of household members exhibited a stronger relationship with water consumption. Thus, they were located on higher branches of the final decision tree. Additionally, results of global Moran’s I index indicated that there exists a spatial autocorrelation among household water consumption data. Moreover, this index indicated the clustered nature of residential water consumption distribution in Babol County. Also, spatial distribution of domestic water consumption in this County demonstrated that western and coastal areas with minimum distance from Babolrood River have the highest level of domestic water consumption. Therefore, it can be concluded that with an increase in distance from Babolrood River, domestic water consumption decreases. Only terraced and semi-detached houses exist in these neighborhoods. Thus compared to other neighborhoods, they have a lower population density, larger green space and larger yard. Conclusion and Future Works The present study applies Apriori algorithm to extract association rules and discover the relationship between spatial and socio-economic parameters and domestic water consumption. Results indicated that spatial and socio-economic parameters affect the spatial distribution of domestic water consumption in Babol County. Developing a decision tree, parameters associated with domestic water consumption were categorized and amount of water consumption was predicted. Extracted rules predicted domestic water consumption of test data with an accuracy of 75%. In this study, global Moran’s I index indicated the existence of a spatial autocorrelation among water consumption data. It also proves the clustered nature of domestic water consumption distribution in the study area. Additionally, spatial distribution of domestic water consumption in Babol County indicated that western and coastal neighborhoods have the highest level of domestic water consumption, while southern neighborhoods of Babol County have the lowest level of domestic water consumption. Model developed in the present study provides an opportunity for analyzing and predicting the level of water consumption. This will make planning for the reduction of water consumption and management of water resources possible. We suggest that future works evaluate the effect of other spatial and socio-economic parameters such as water cost and educational status of household members in a longer period (more than 5 years) to improve the accuracy of the model.
Hasanali Faraji Sabokbar; Seyyed Hasan Motiee Langroodi; Hossein Nasiri
Abstract
Abstract
With the development of science and technology, a large amount of spatial and non-spatial data are stored on large databases. Analyzing these data for decision making necessitates the need for spatial data mining to discover knowledge. The use of satellite imagery, geo-statistical analysis, ...
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Abstract
With the development of science and technology, a large amount of spatial and non-spatial data are stored on large databases. Analyzing these data for decision making necessitates the need for spatial data mining to discover knowledge. The use of satellite imagery, geo-statistical analysis, and all types of spatial data are useful and practical tools in studying land use change monitoring; but, what is important is the extraction of precise rules by integrating large amounts of data in order to provider knowledge about the area of interest. Rough Set Theory (RST) is one of the data mining techniques used in various ways in modeling uncertainty in data. Therefore, in this research, the RST knowledge discovery method is used to extract rules in combination with decision tree algorithm (DT) for satellite image classification and monitoring of land use changes. The results of the research indicate that according to the changes occurred during three periods of (1986-1998, 1998-2014 and 1986-2014), it can be seen that significant increasing and decreasing changes have occurred in the constructed lands and in the water bodies, while agricultural lands have not changed much. Of course, considering the base year (1986), it can be stated that the area of the agricultural lands under cultivation has witnessed a slight change compared to the base year which coincided with the imposed war, which means that the area under cultivation during the past three decades has been the same as that of the war period. This indicates that, the crisis is taking place in the agricultural sector. Also, in terms of methodology, given the overall accuracy and Kappa ratio, derived from the DT-RST combination model, RST can be considered to be a powerful tool in data mining, reducing the redundant data from databases and extracting rules for use in the DT method.
