spatial data mining

Usually, geographical complications have a kind of adhesion to each other, and one complication attracts other complications. In other words, these collections are placed together so that as a result of gathering and settling in the vicinity, they complement each other and meet the needs of customers. The composition of such collections is influenced by economic, social, cultural, political, planning, physical factors, etc. To carry out this research, the location of branches and urban financial and credit institutions of Tehran was used, 3773 branches were used as a location for collecting data and making a collection of items. 56 classes of urban complications were considered for data analysis and extraction, and all complications that were within 200 meters of branches were extracted and then analyzed using a priori method. In this research, a set of rules were extracted that put different types of activities together. These rules show a better understanding of compatible activities, complementary activities, and previous and previous links of different types of activities

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.

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.

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.

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.

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.

The inclusive city approach focuses on vulnerable groups, minorities, deprived and rejected individuals, and calls for justice and the reduction of inequality in various fields. The main goal of this study is to measure and spatial analysis of the Inclusiveness of Tehran Urban spaces to assess the Accessibility of vulnerable groups to urban services. Vulnerable People include women, children, the elderly, the illiterate, the unemployed, immigrants, and families with disabilities who are assessed as target groups. The method used in this research is quantitative and by comparing and validating three different methods of spatial data mining methods Such as principal component analysis, spectral clustering, and self-organizing maps. Validation and evaluation of different options in clustering have been done using 4 internal clustering evaluation indicators and comparison between the best method and the most optimal number of clusters. Results, show the self-organizing maps method and also 5 number of clusters have been selected as the best option from other options. Based on the final outputs, about 31 percent of the residential area in Tehran has a lower than average accessibility, most of which are located in the marginal spaces and near the border of the city. Also, most of the central and northern areas of Tehran have good and very accessible access, which constitutes nearly 40percent of Tehran's residential areas. The results of the analysis of vulnerable groups also indicate that more than 30 percent of women, children and the illiterate live in areas with lower than average accessibility levels.

Management and planning of urban water supply in metropolis is very important. Development of the region urban and make cities to metropolis and increase of effective complex factor on water usage in the cities make consumption management and supply and distribution Water difficult. So rule extraction plays an important role in exploring patterns over data and decreasing complex. Rough Set Algorithm, which was developed in 1980s by Pawlak, is a powerful and flexible method to deal with uncertain and ambiguous data which has been used in this research to extract dominant rules over data set. The method used in this paper is combination of the rough set and genetic algorithms from data mining methods to develop rule extraction and data classification of water usage in Tehran city as the studying area. Socio-economic, environmental, time and water consumption and management zones have been used as the explanatory variables for prediction of the water use that database divided to 2 part 60% for result extraction and 40% as test set. Independent test sets have been used for evaluation of the accuracy of the extracted rules. Results have shown that, combination of the genetic algorithms and Rough Set leads to extraction of more reliable rules. Classification accuracy of the extracted rules from Rough sets was 77 percent. But optimization of rules by combination of the genetic algorithm with Rough sets, resulted in classification accuracy of 88 percent in 6th generation with average speed of convergence. By using the same speed of convergence in the accuracy increased to 92 percent in 10th generation. According to the extracted rules, important effective factors on annual water consumption are respectively the resident population, water price, population density, family size, spatial location (latitude), education levels, and per capita green spaces.