Spatial Differentiation and Influencing Factors of Second-Hand Housing Prices: A Case Study of Binhu New District, Hefei City, Anhui Province, China

The multiscale geographic weighted regression (MGWR) model obtains different influence scales of various variables better than the classical geographic weighted regression (GWR) model. This paper studies the price characteristics of second-hand residential transactions in Binhu New District taking advantage of the hedonic price model and MGWR model and draws the following conclusions. (1) There are obvious spatial positive correlation and spatial heterogeneity in the price of second-hand housing in Binhu New District. (2) The number of bedrooms, area, age of the house, and the distance to the nearest school have small effect on the scale, so they have strong spatial heterogeneity. The decoration status and floor are global scale variables, and their spatial heterogeneity is weak. (3) The number of bedrooms, orientation, decoration status, floor, and building structure all positively affect house prices, while area, house age, the distance to the nearest subway station, and the distance to the nearest school negatively affect house prices. Among all factors, the distance to the nearest school is the most important factor affecting house prices, followed by the number of bedrooms and then followed by the distance to the nearest subway station and area, while the orientation, floor, building structure, and decoration conditions have less impact, and the house age has the weakest impact.

Understanding and Modelling the Impacts of a New Competitor on the Sales of Established Businesses Within Similar Markets

The research conducted for this study presents a Geographic Information Systems (GIS) and spatial analysis to understanding and modelling the impacts of a new competitor on the sales of established businesses within similar markets. Data for this analysis was provided by Canadian Tire Corporation (CTC).This study will outline and create trade areas for all stores in the Part Source network to determine which stores will be the focus of this study. Once focus stores have been determined, sales data was analyzed in two ways. The first analysis was conducted to determine the distance decay effects of the new competitor on sales. The second analysis utilized geographic weighted regression (GWR) models on expenditure and vehicle data.

Understanding and Modelling the Impacts of a New Competitor on the Sales of Established Businesses Within Similar Markets

The research conducted for this study presents a Geographic Information Systems (GIS) and spatial analysis to understanding and modelling the impacts of a new competitor on the sales of established businesses within similar markets. Data for this analysis was provided by Canadian Tire Corporation (CTC).This study will outline and create trade areas for all stores in the Part Source network to determine which stores will be the focus of this study. Once focus stores have been determined, sales data was analyzed in two ways. The first analysis was conducted to determine the distance decay effects of the new competitor on sales. The second analysis utilized geographic weighted regression (GWR) models on expenditure and vehicle data.

Spatial dynamics of the COVID-19 pandemic in Brazil

The objective of this study was to analyse the dynamics of spatial dispersion of the coronavirus disease 2019 (COVID-19) in Brazil by correlating them to socioeconomic indicators. This is an ecological study of COVID-19 cases and deaths between 26 February and 31 July 2020. All Brazilian counties were used as units of analysis. The incidence, mortality, Bayesian incidence and mortality rates, global and local Moran indices were calculated. A geographic weighted regression analysis was conducted to assess the relationship between incidence and mortality due to COVID-19 and socioeconomic indicators (independent variables). There were confirmed 2 662 485 cases of COVID-19 reported in Brazil from February to July 2020 with higher rates of incidence in the north and northeast. The Moran global index of incidence rate (0.50, P = 0.01) and mortality (0.45 with P = 0.01) indicate a positive spatial autocorrelation with high standards in the north, northeast and in the largest urban centres between cities in the southeast region. In the same period, there were 92 475 deaths from COVID-19, with higher mortality rates in the northern states of Brazil, mainly Amazonas, Pará and Amapá. The results show that there is a geospatial correlation of COVID-19 in large urban centres and regions with the lowest human development index in the country. In the geographic weighted regression, it was possible to identify that the percentage of people living in residences with density higher than 2 per dormitory, the municipality human development index (MHDI) and the social vulnerability index were the indicators that most contributed to explaining incidence, social development index and the municipality human development index contributed the most to the mortality model. We hope that the findings will contribute to reorienting public health responses to combat COVID-19 in Brazil, the new epicentre of the disease in South America, as well as in other countries that have similar epidemiological and health characteristics to those in Brazil.

Accessibility, Infrastructure Provision and Residential Land Value: Modelling the Relation Using Geographic Weighted Regression in the City of Rajkot, India

Amenities and infrastructure provision in urban areas are essential for the sustainable future of cities in developing countries like India. Indian cities have large development deficits and find it challenging to bridge the gap using traditional methods. Provision of these facilities costs money, which is often not available. However, access to amenities and infrastructure adds to land premium, which, if captured, can be used to finance the provision of these facilities. In India, very little information is available on the value of accessibility and infrastructure provision, and thus, these indirect benefits are primarily ignored by urban planners. This study fills the gap by identifying these benefits using Rajkot city in India as a case study. A geographic weighted regression model is used to model the relationship. It is found that land price variation is explained to a good extent using the model. Estimates show that infrastructure and amenities have a substantial impact on land value, much higher than the cost required to provide these.

Lack of Spatial Approach in Urban Density Policies: The Case of the Master Plan of Tehran

This paper aims to investigate the approach of density policies in the Tehran Master Plan and the consequences of ignoring the macro spatial scale in density policymaking. In this study, the floor area ratio (FAR) regulations of the Master Plan of Tehran (which are defined by specific land use zones) are used as one of the main densification tools. Then, employing the Getis–Ord Local G and geographic weighted regression (GWR) statistical tests, Arc GIS 10.3 software, and population and employment variables, the spatial outcomes of the Master Plan density policies were modeled. In this research, both population and employment (job) variables and their relationship were utilized to depict the urban spatial structure of the city. The model will show the resulting spatial structure of Tehran if the densification policies of the plan are realized. The findings of the research are surprising, as they indicate that the Master Plan’s densification policies would worsen the current spatial structure by disrupting the current population and employment spatial structure and neglecting their logical relationships. In fact, the Master Plan would change the current polycentric structure into a highly dispersed structure due to its densification approach, which is mainly based on the neighborhood micro scale.

IMPROVED INDOOR POSITIONING TECHNIQUE BASED ON A GEOGRAPHIC WEIGHTED REGRESSION

As technology and science develops and the coming of new equipment’s, standards and different waves spread. Each of these standards and technologies have involved in indoor positioning by various scholars. Various methods have been developed based on different systems, all of which are based on specific methods and concepts. The research tries to do indoor positioning using local Wi-Fi fingerprints and signals. To reduce the error to collect local fingerprints, RSS values are recorded in 4 directions and two times. Geographic weighted regression method has been used to train the network. In this research, a genetic algorithm is used to select the appropriate parameters. Ultimately, the accuracy of the model has reached 1.76 cm. The results show that the increase in the number of access points does not affect the accuracy of position determination, but the choice of the effective access point will be effective in reducing the error.

Modelling Sustainable Urban Transport Performance in the Jakarta city Region: A GIS Approach

Measuring performance of Sustainable Urban Transport is an important effort to face the challenges of future trends. This study used Geographic Information System (GIS) application for modelling the performance of Sustainable Urban Transport (SUT) in the Jakarta city Region (JCR). The GIS applications include identifying the existing performance of SUT in Jakarta city, exploring the relationships between indicators of SUT, and producing a prediction model of SUT. Research methods used in this study were GIS techniques with geo-reference, classifications, polygon to raster, re-classifications, sum-weighted, ordinary least square (OLS), exploratory regression, and geographic weighted regression (GWR). The results revealed that the SUT model have more capability in measuring the performance of SUTs spatially and simply. This model is to visualize the effect of the indicator on the SUT performance and its influence, respectively. The results of this study also discovered that the JCR’s Sustainable Urban Transport Performance was in medium level. The outputs of this modelling were useful for evaluating the level of SUT performances in the city based on districts area. Overall, this study provides valuable information on the SUT performance of the JCR, also highlights some important challenges faced in the future of SUT program development.