Car parking is a challenging part of urban transportation and the traffic violations around it cause many problems for citizens. In recent years, due to the fast growth and development of urbanization, temporary and unauthorized stopping of cars along the streets, especially in large cities, has led to an increased traffic, urban disorders, dangers for citizens, and violation of rules. Studies have shown that there is a direct relationship between vehicle parking violations and urban places. GIScience capabilities and tools play an important role in analysing the spatial distribution of these violations. In this study, we investigated the spatial distribution of vehicle violations in a region of Tehran, Iran that is suffering from a heavy traffic load and heavily polluted air. Although two dissimilar urban segregations exist in the north and south of the study area, our analysis indicates a similar pattern of car parking violations. In both of the areas, about 70% of all curb parks are legal, while the remaining are illegal. Also, spatial analysis reveals a direct relationship between some POIs and the occurrence of car park violations so that the density of legal curb parks is high near some POIs, and less near some others and vice versa. For example, the number of vehicle park violation around the hospitals is more than the average of the study area. However, the number of park violations around the universities is less than the average. Our findings reveal that co-location of certain POIs, for instance a hotel and a supermarket will lead to an increase in the number of park violations. In other words, there is a strong correlation between the type of POIs and curb-parks violations. Our results also show that POIs have an impact radius that leads to violations occurring in that area. For example, the area of the impact of a hospital on the creation of car park violations was estimated at 125 meters. Our presented approach along with the discussed findings along with conclusions can be useful to a large range of stakeholders including urban planner, traffic police departments, local municipalities, law enforcement agencies, and environmentalists to have a better perspective of infrastructure planning.

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Car parking is a challenging part of urban transportation and traffic violations cause many problems for citizens. Studies have shown that there is a direct relationship between vehicle parking violations and urban places. In this study, we investigated the spatial distribution of vehicle violations in a region of Tehran, Iran, that is suffering from a heavy traffic load and heavily polluted air. Although there are two dissimilar urban segregations in the north and south of the study area, in both of the regions, about 70% of all curb-parks are legal, while the remaining are illegal. However, our analysis indicates a dissimilar pattern of car parking violations. Additionally, spatial analysis reveals a direct relationship between some POIs (Point of interests) and the occurrence of car park violations. For example, the number of vehicle park violations around the hospitals is more than the average of the study area. However, the number of park violations around the universities is less than the average. Our results also show that POIs have an impact radius that leads to violations occurring in that area. For example, the influence range of a hospital on the creation of car park violations was estimated at 125 meters. Our presented approach along with the discussed findings and conclusions can be useful to an extensive range of stakeholders, including urban planners, traffic police departments, local municipalities, law enforcement agencies and environmentalists, to have a better perspective of infrastructure planning.

The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed that the accuracy of satellite data varies significantly under drought, wet, and normal conditions and different timescales, being lowest under drought conditions, especially in arid regions. The highest accuracy was obtained on the 12-month timescale and the lowest on the 3-month timescale. Although the accuracy of the data is dependent on the season, the seasonal effects depend on climatic conditions.

Purpose Housing price is a barometer of a national economy. In recent years, Iran experienced high inflation in its economy, which affects everything, including housing. The purpose of this study is the estimation of the value of residential apartments of Tehran using ordinary least square (OLS) and geographically weighted regression (GWR) methods. Design/methodology/approach This paper proposed a method for determining the compound variables and used them to estimate and evaluate the prices in the district six of Tehran city. Also, this paper compared the GWR and OLS methods with different types of factors and their influences in house price estimations. Findings During the high inflation period of the study period, the age of buildings, inflation, parking, storage room and their locations are the most critical factors that affect the price of apartments in district six of Tehran. Besides, compound variables have the most influence on the prediction of the prices. Research limitations/implications The exact location of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Practical implications The exact locations of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Originality/value The originality of the proposed method is that it used a different approach to determine the valid variables of the apartment prices. Also, the evaluation of the method showed that the proposed variables are significantly useful.