پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,948,966 |
| تعداد دریافت فایل اصل مقاله | 89,620,398 |
کاوش الگوهای پرتکرار فضایی فعالیت‌های شهری مطالعه موردی: بانک‌ها و مؤسسات مالی و اعتباری شهر تهران | ||
| پژوهش‌های جغرافیای برنامه‌ریزی شهری | ||
| مقاله 3، دوره 11، شماره 4، بهمن 1402، صفحه 49-65 اصل مقاله (1.37 M) | ||
| نوع مقاله: پژوهشی - کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jurbangeo.2023.347390.1727 | ||
| نویسنده | ||
| حسنعلی فرجی سبکبار* | ||
| گروه جغرافیایی انسانی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| معمولاً عوارض جغرافیایی دارای نوعی چسبندگی به هم هستند و یک عارضه در کنار خود سایر عوارض را جذب می‌کند. به عبارتی این مجموعه‌ها در کنار هم قرار می‌گیرند تا در نتیجه تجمع و استقرار در مجاورت هم مکمل یکدیگر بوده و نیازهای مشتریان را تأمین نمایند. ترکیب چنین مجموعه‌هایی تحت تأثیر عوامل اقتصادی، اجتماعی، فرهنگی، سیاستی، برنامه‌ریزی، کالبدی و.. قرار دارند. برای انجام این تحقیق از موقعیت شعب و مؤسسات مالی و اعتباری شهری تهران استفاده‌شده است، 3773 شعبه به‌عنوان موقعیتی برای جمع‌آوری داده‌ها و ساخت مجموعه اقلام استفاده شد. 56 کلاس عارضه شهری برای تحلیل و استخراج داده‌ها در نظر گرفته شد و کلیه عوارضی که در محدوده 200 متری شعب قرار داشتند استخراج شدند و سپس با استفاده از روش اپریوری مورد تحلیل قرار گرفتند. در این تحقیق مجموعه‌ای از قواعد استخراج شد که نحوه قرارگیری انواع فعالیت‌ها را در کنار یکدیگر قرار می‌دهند این قواعد به درک بهتر فعالیت‌های سازگار، فعالیت‌های مکمل، پیوندهای پسین و پیشین انواع فعالیت‌ها را نشان می‌دهد | ||
| کلیدواژه‌ها | ||
| داده‌کاوی فضایی؛ کاوش الگوهای پرتکرار؛ مجموعه اقلام پرتکرار؛ مجموعه اقلام | ||
| عنوان مقاله [English] | ||
| Mining frequent spatial patterns of urban activities The Case study a Banks and financial and credit institutions of Tehran | ||
| نویسندگان [English] | ||
| HassanAli Faraji Sabokbar | ||
| ) Department of Human Geography, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| ABSTRACT 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 Extended Abstract Introduction The subject of geography is the study of the distribution and spatial arrangement of geographic objects. Objects are not usually randomly distributed, but often have specific spatial patterns. Some geographic phenomena are interdependent and often complement each other, while some activities support other activities. Understanding these spatial connections and recognizing patterns helps us better understand the spatial distribution of elements. Activities are attracted to each other through the links they have. These links address each other's needs through backward and forward communications and may generate demand for various services. Bank branches, by attracting resources and providing various credit services, engage in service provision and activities. They prefer to be located in places with high resource attraction, such as proximity to large commercial complexes, market corridors, hospitals, and other service centers (business factors). Therefore, the distribution of bank branches is somewhat dependent on other activities, especially those with higher financial turnover. In addition, many urban services are somewhat complementary or competitive to each other. Moreover, in commercial corridors in cities, they play an important role in attracting customers. Depending on the scale of activities and the volume of businesses, they can have penetration areas with different extents. Methodology Machine learning based on association rules (Apriori algorithm) has been used for research purposes to explore recurring or repetitive patterns. It has been used to analyze branches of banks and financial institutions in Tehran. For the research, the data is divided into two categories: data related to banks and financial institutions, which constitute the basket or set of items. To this end, 3773 bank branches (including Export, National, Maskan banks, etc.) were selected, and maps and information layers were generated for them. The second part is about urban incidents in Tehran. The information was categorized, and their maps were collected and entered into the system. 81066 urban incidents were investigated, including healthcare facilities, public places, city squares, etc. To extract information items about the branches, first, a 200-meter radius was set for each branch. In the next step, the incidents that occurred within this range were extracted and entered into a matrix. The research findings indicate that the distribution and dispersion of bank branches in Tehran are not uniform. The central parts of Tehran have the highest number of branches. The northern part of Tehran has been affected by the placement of offices and service centers along Valiasr Street, pulling them away from the center towards the north. The southern part of Tehran is mostly occupied by government or government-private banks (such as Bank Melli, Maskan, Mellat, Sepah, and Tejarat). The western part of Tehran has a low number of branches. Results and discussion According to the research findings, the highest frequencies in the range of bank branches are related to insurance, restaurants, official document offices, pharmacies, etc., while the lowest frequency is related to hospitals and emergency centers. The set of recurrent items and association rules contain valuable information for spatial analysis and decision-making in urban areas. For example, in the above table, the first row indicates the association between hospitals (Ph) and insurance offices (Ic) with a support of 52%, and the next item is the official document office (Np). This means that if there are hospitals and insurance offices in the vicinity of a bank branch, there will also be an official document office. Another rule (1618961) with a support of 1 and a confidence of 70% includes the pharmacy (Ph) as the antecedent and physiotherapy, clinics, radiology, medical diagnostic laboratories, newspaper offices, and government facilities as the consequent. In other words, when there is a pharmacy within a 200-meter radius of a bank branch, we expect these 7 items to be located as well. The rule (3086569) with a support of 1 and a confidence of 70% includes the official document office (Np) as the antecedent and marriage and divorce offices (Md), insurance offices (Ic), travel agencies (Ta), government facilities (G), restaurants (R), and medical diagnostic laboratories (La) as the consequent. Therefore, we expect the types of services listed in the consequent to be present where there are official document offices. Another rule with three antecedents and three consequents, a support of 5%, and a confidence of 67% has the pharmacy (Ph), clinic (Cl), and hospital (Ho) as the antecedents, and medical diagnostic laboratories (La), emergency centers (Uc), and radiology (Ra) as the consequents. These items are of the same kind and indicate that the presence of a hospital, pharmacy, and clinic as the antecedent attracts complementary services and centers such as medical diagnostic laboratories, emergency centers, and radiology. In other words, there is a link and correlation between these activities in urban areas, and exploring and discovering such relationships can provide a better understanding of urban activities and processes, as well as the connection and interaction between services. It offers guidelines for urban planning to gain a better understanding of economic and social activities in the city, identify complementary or competing services, discover urban processes, and facilitate intelligent urban management. Current models of planning usually rely on literature and theories about cities, while the discovery of activity and service distribution patterns and their associations and dependencies can provide a more realistic understanding of urban connections. Conclusion In this study, the spatial distribution and establishment of geographical features within the branches of banks were analyzed, and it was determined what types of activities dominate within the vicinity of each branch. Based on this, spatial linkages between the features could be identified, and complementary and supplementary geographical features could be identified accordingly. For future research, it is suggested that these patterns be examined locally to extract the background effects on the patterns. In this study, Tehran as a whole has been considered uniformly, but in future research, spatial differences need to be addressed, geographical analysis should be added, and other algorithms for exploring recurring patterns should be introduced. Additionally, attention should be paid to other urban features besides banks. Funding There is no funding support. Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of Interest Authors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper. | ||
| کلیدواژه‌ها [English] | ||
| Spatial Data Mining, Frequent Pattern Mining (FPM), Frequent itemset, Itemset | ||
| مراجع | ||
| ||
|
آمار تعداد مشاهده مقاله: 206 تعداد دریافت فایل اصل مقاله: 16 |
||