7143 Sayılı Vergi ve Diğer Bazı Alacakların Yeniden
Yapılandırılması ile Bazı Kanunlarda Değişiklik Yapılmasına İlişkin Kanun
18/05/2018 tarihinde Resmi Gazete'de yayımlanarak, kamuoyunda "İmar
...Barışı" olarak adlandırılan süreç uygulanmaya başlanmıştır. 3194 sayılı İmar
Kanunu'na eklenen Geçici 16. maddeyle, imar düzenine aykırı yapıların
"Yapı Kayıt Belgesi" ile kayıt altına alınması amaçlanmaktadır. Yapı
Kayıt Belgesi, kentsel dönüşüm uygulamalarının finansal kaynağını yaratmak
üzere, imar düzenine aykırı yapıları kayıt altına alırken yapı sahiplerine de
kentsel dönüşüm uygulamasına kadar yapıyı kullanma hakkı vermektedir. Bu
yönüyle Geçici 16. madde, idarenin afet riskine karşı alacağı tedbirler için
yetersiz; fakat imara aykırı yapıların tespitini sağlaması açısından
yerindedir.
Çalışmamız
kapsamında Yapı Kayıt Belgesinin mülkiyet hakkı ile ilişkisi kurularak,
maddenin bir imar affı olup olmadığı; imar düzenimiz içinde ortaya çıkan benzer
uygulamalarla farklılıkları; kamu malları rejimi bağlamında kapsamı incelenmiştir.
Afet risklerine karşı tedbir alma yükümlülüğü idarenin asli görevlerinden
olduğundan, söz konusu düzenlemeyle depremle yıkılan yapılar nedeniyle idarenin
sorumluluğunun, yapı sahibine devredilemeyeceği de ortaya konulmuştur.
Law No. 7143 on the
Restructuring of Taxes and Other Receivables and Amendments to Certain Laws was
published in the Official Gazette on 18/05/2018 and the process called
"Zoning Peace" started to be implemented in public. With the Article
16 of the Law No. 7143, appended as the Provisional Article 16 to the Zoning
Law No. 3194, it is aimed to take the buildings contrary to the zoning order
under registry with the "Building Registration Certificate". Building
Registration Certificate in order to create the needed financial source for the
urban transformation applications, while
registering the buildings that does not comply with the zoning regulations,
also gives the building owners the right to use the building until the urban
transformation ends. In this aspect, Temporary Regulation No 16 is both,
inadequate for the measures that will be taken by the administration against
the risk of catastrophes and efficient in order to detect the buildings that are
violating the zoning regulations.
In this article, by
focusing on the relationship between Building Registration Certificate and the
right to property, whether the related regulation is a zoning peace regulation
or not, its differences from the similar applications in our zoning order and
the scope of it in the context of public estate will be examined. Since the
obligation to take measures against disaster risks is the primary duty of the
administration, conclusion is drawn as the responsibility of the administration
arising from the structures destroyed by an earthquake cannot be transferred to
the building owner as per the mentioned regulation.
This study resolved the problem of Part 2 and refined the method for estimating the spatial distribution of vacant houses using public municipal data by addressing the following issues. 1) This ...study realized a more reliable method for estimating the distribution of vacant houses without concern for the simplicity of the method used in Part 2 2) We applied the method developed in this study to the entire area of Kagoshima City and Asakura City covered by Part 2, clarifying the method’s practicality and versatility. 3) By comparing the results of Part 2 and this study, we demonstrated the extent to which the reliability of this paper’s estimation method was improved. 4) The correct answer rate on a building-by-building basis was also revealed, and the results of estimating vacant and non-vacant houses per building could be obtained. Chapter 2 introduced the database for analyzing the characteristics of vacant houses: the vacant house database, developed by combining several public municipal data: the basic resident register (BRR), Hydrant consumption amount information (HCI), and the building registration information (BRI) and the results of a field survey of vacant houses, as used in Part 2. Chapter 3 introduced the method for estimating the distribution of vacant houses in Part 2 and its challenges. Chapter 4 proposed a method for estimating the distribution of vacant houses. In this study, XGBoost, a decision tree-based machine learning model for dealing with missing values and setting optimal thresholds, was applied to estimate the vacant house probability of each building. XGBoost iterated the decision tree until there was no improvement in predictions, and then summed the results of each decision tree to estimate the vacancy probability per building. Chapter 5 verified the reliability of the method proposed in this study, comparing it with the method developed in Part 2. Results show that the correct answer rate for the vacant or non-vacant judgments for each building reached 97.81% in Kagoshima City and 97.25% in Asakura City. Even when we aggregated by the 250-m square grid as in Part 2, the accuracy of this study’s methods exceeded that of Part 2. In addition, when we determined whether a building was vacant or not using the method of Part 2, the determination accuracy of vacant houses was particularly low. However, the method used in this study significantly improved this problem and could estimate both vacant and non-vacant buildings with high accuracy. Finally, Chapter 6 showed the estimated number of vacant houses and the vacant house rate aggregated by a 500-m square grid for Kagoshima and Asakura. Compared to the results of Part 2, the estimated number of vacant houses in Kagoshima City increased from 7,361 to 9,856, while the number of vacant houses in Asakura City remained almost the same. In addition, vacant house rates were higher in the central city and mountainous areas and lower in the suburbs than in Part 2 for both cities. This indicates that it is possible to estimate the distribution of the number of vacant houses accurately.
Vacant housing detection is an urgent problem that needs to be addressed. It is also a suitable example to promote utilisation of smart data that are stored in municipalities. This study proposes a ...vacant housing detection model that uses closed municipal data and considers accelerating the use of public data to promote smart cities. Employing a machine learning technique, this study ensures high predictive power for vacant housing detection. The model enables us to handle complex municipal data that include non-linear feature characteristics and substantial missing data. In particular, handling missing data is important in the practical use of closed municipal data because not all of the data are necessarily absorbed to a building unit. Consequently, the model in this analysis showed that the accuracy and false positive rate are 95.4 percent and 3.7 percent, respectively, which are high enough to detect vacant houses. However, the true positive rate is 77.0 percent. Although the rate is not low to some extent, selection of features and further collection of extra samples may improve the rate. Geographic distribution of vacant houses further enabled us to check the difference between the actual and estimated number of vacant houses, and more than 80 percent of 500-meter grid data are with below 10 errors, which we think, provides city planners with informative data to roughly grasp geographical tendencies.
Monitoring of the spatial distribution of vacant houses across broad areas for vacant house measures by Japanese local governments requires substantial labor, time, and money because the main survey ...method is visual inspection via field surveys. Therefore, we developed the method for estimating the spatial distribution of them using various municipal public data and sample field surveys in the paper of part1. This paper aims to improve our previous method and increase the estimation accuracy. The target area was the whole area of Kagoshima city and Asakura city shown in Fig. 1 and Fig. 2. In chapter 2, we detected the spatial distribution of vacant houses via field surveys by visual inspection in the sample field survey areas in Fig. 1 and Fig. 2, and developed the vacant house database for developing the estimation method of vacant house distribution. The vacant house database has results of field survey shown in Table1 and attributes shown in Table2 of each detached building. Municipal public data included the database are the basic resident register (BRR), Hydrant consumption amount information (HCI), and the building registration information (BRI). In chapter 3, we developed the estimation method of vacant house distribution. The method first developed crosstab tables with the attributes of Table2 as explanatory variables, secondly calculated the vacancy rate for each combination, and finally estimated the vacancy rate of each building by allocating the vacancy rate of the table for each building. At first, we developed the crosstab tables by cell splitting of each explanatory variable shown as Table 5. Second, as shown in Table6, because of comparing the number of vacant house every 250 m square grid with the true value while decreasing the number of used variables, it was revealed that the estimation result by the crosstab table using all the variables is the most reliable. In addition, it was also revealed that the HCI has high interpretability shown in Fig. 4. By comparison with estimation result of the method in the part 1, it was confirmed that estimation accuracy improved considerably as shown in Fig. 5 and Fig. 6. Third, as a result of estimating Asakura city by the crosstab table developed from the field survey result of Kagoshima city, there were some grids with large error although the high estimation accuracy was obtained as shown in Fig. 7. Therefore, we estimated using a crosstab table developed using the results of field survey conducted by both cities. As a result, high estimation accuracies were obtained in both cities: the correlation coefficient was about 0.95, and the mean absolute error was about 1.0 as shown Table7. From this result, it was expected that by collecting the field survey results and public data of more municipalities in the future, it is possible to estimate vacant house distributions with higher accuracy in various municipalities. Finally, we accomplished to estimate the spatial distribution of vacant houses in arbitrary spatial unit in Chapter 4. Fig. 8 to Fig. 11 show the estimated results of the number of vacant houses and rates aggregated into 500m square grids of Kagoshima city and Asakura city. The results estimated that 7, 361 detached buildings were vacant of the 270, 631 detached buildings, i.e. 2.72% of houses were vacant in Kagoshima city, and 2, 299 detached buildings were vacant of the 44, 005 detached buildings, i.e. 5.22% of houses were vacant in Asakura city.
Monitoring of the spatial distribution of vacant houses across broad areas by local governments requires substantial labor, time, and money because the main survey method involves visual checks via ...field surveys. This is a major obstacle for local governments when developing means to measure the number of vacant houses. Therefore, we developed a method for estimating the spatial distribution of vacant houses using varied spatial data and sample field surveys of part of the target area instead of a field survey of the whole target area to reduce the cost and labor required for the vacant house survey. The target area was the city center of Kagoshima city, Kagoshima Prefecture, shown in Fig. 1. In chapter 2, we detected the spatial distribution of vacant houses via field surveys according to the criteria for vacant and non-vacant houses shown in Table 3. This was conducted in sample field survey areas that contained 3, 134 detached buildings, shown in Fig. 1 and in Table 2. Table 4 shows the results of the field survey in the sample field survey areas. The table shows that there are 173 detached vacant houses in these areas. In chapter 3, we developed the vacant house database to first integrate the results of the field survey, digital residential map, and municipal public data that included the basic resident register, closed hydrant information, and the building registration information. Second, we calculated the number of vacant houses and rates of building use for detached buildings based on the presence or absence of public data using the vacant house database shown in Table 5. The number of vacant houses in each 500m square grid could be estimated by applying these values. Finally, we calculated vacant house scores for all of the detached buildings based on Table 6. Detached buildings were assigned as vacant houses in descending order of the vacant house score until meeting the number of vacant house by 500m square grids. Scores tended to be larger for smaller numbers of people per household and building area, larger proportions of older residents, the maximum and minimum duration of residence, and earlier construction year. Using this method, the spatial distribution of vacant houses in the target area could be estimated. In addition, we verified the reliability of our method. We compared the number of actual vacant houses monitored by field surveys in the sample field survey areas with the estimated number of vacant houses calculated by our method, accumulated within 500m square and 250m square grids. Table 7 shows the comparison results for the case of a 500m square grid and Table 8 the case of a 250m square grid. Both set of results showed that there were no significant differences between actual values and our estimated values. This indicates that the method proposed in this study can estimate the spatial distribution of vacant houses with good accuracy. Chapter 4 introduced the results for the estimation of the spatial distribution of vacant house in the target area i.e. the city center of Kagoshima city. Fig. 4 to Fig. 7 show the estimated results of the number of vacant houses and rates aggregated into 500m square grids and city blocks. The results estimated that 1,740 detached buildings were vacant of the 32,448 detached buildings in the target area, i.e. 5.36% of houses were vacant. Our method can estimate the number of vacant houses and their rates to aggregate them into spatial units according to the objective.
Land use change in industrial district is dramatically increasing in Korea.. Current land use change in industrial district in Busan, Korea is planned based on "a policy of production increase per ...labor" under "Busan master plan 2011" to increase residential land use. Under the depopulation and unemployment, the land use change planning from industrial use to residential use is not appropriate. It is important to discuss about the effienciency of the land use change policy. The aim of this study is to analyze current land use change in industrial district. For this purpose, the building registration data in Sasang which is a typical industrial district in Busan, Korea, is collected and analyzed by GIS. The building registration data includes zoning and property deed data. From the analysis, this study found that the land use change was occurred before the policy-making. Finally, this study discussed a policy implement.
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