One of the important aspects of development from the spatial perspective is the evaluation of the effective long-term processes on the institutional mechanisms of a land and the determination of share of the respective areas in this regard. For many years, economy has been hurriedly moving toward a knowledge-based stance, and the countries that have neglected this movement have been deprived of fundamental developments. In this applied study, which adopts an analytical approach and a quantitative view, the necessity of the proposition of a strategic attitude to the two-way relationship between higher education and the industry sector of Iran is addressed. To this end, first an overview of the previous studies and theoretical principles are made. Then, following the questions and hypotheses and determining the evaluation and analysis methods, the detailed information on over 11 variables (including panel data of 31 provinces of Iran from 2016 to 2019) are collected. These are then analyzed and tested using Excel, Eviews, and Arc GIS through spatial econometrics, generalized method of moments (GMM), weight matrix and geographic-spatial correlation (λ), and KP-HET diagnostic tests. According to the results, the spatial correlation between higher education and industry in Iran is highly significant, and the shock resulting from the industrial and educational development is completely mutual. That is to say, the shock inflicted upon a given province has spread to other provinces of Iran. The findings of this study confirm the effects of the emphasis on the centralization policy stereotype and the prescriptive – rather than land-use-based – roles given to certain parts of Iran. In fact, the very high spatial correlation demonstrates that the nine less-developed provinces of Iran have experienced trivial industrial growth due to a lack of higher education infrastructure. On the other hand, the results of this study shows how the market demand for labor of the university graduates is aligned with the functions of the knowledge-based industries of Iran.

The increasing population growth along with human activities has led to the booming of waste production.  The selection of inappropriate landfill sites contributes to the increased potential of underground pollutions. The purpose of this study was to zone the underground water vulnerabilities of Karun County in Khuzestan Province to select the optimal landfill site, so that a site with the least vulnerability of aquifer would be selected for waste disposal. Two models were used to zone the aquifer pollution potential, namely GODS and SINTACS. After preparing the vulnerability maps of the two models in GIS software, the appropriateness levels of the sites for waste disposal were prioritized. The results of SINTACS model demonstrated the following vulnerability statistics: 455.22 hectares have no vulnerability potential, 780.84 hectares very low vulnerability, 3281.31 hectares low vulnerability, 12582.4 hectares low to medium vulnerability, 11169.9 hectares medium to high vulnerability, 9449.6 hecaters high vulnerability, 5844.15 hectares very high vulnerability, and 495.72 hectares totally at vulnerability risk. Moreover, in GODS model, nearly 215.576 hectares have negligible vulnerability, 560.544 hectares low vulnerability, 5169.08 hectares medium vulnerability, 37471.9 hectares high vulnerability, and 641.9 hectares very high vulnerability. In both models, the least vulnerable zones comprise a small part of the area (1.03 percent in SINTACS and 0.49 percent in GODS model). This level can be selected as the optimal landfill site (rank 1), while other zones are ranked lower due to their higher aquifer vulnerability levels.

Using a spatial information system to manage real estate data has two advantages: one is the storage of valuable data using a real-world spatial index, and the other is the ability to analyze and visualize this data in a new way by collecting numerous information sources. In this research, using the Survey Analyst extension and the Cadastral Editor tool of ARC GIS software, a property registration map (cadastre) of an area of Kerman city has been prepared as an example. First, the required maps were collected, and then the geo-database of the study parcels of the studied area and their topology were designed. In the next step, the Cadastral Fabric Dataset Layer was created, which is the starting point for working with data related to parcels. By transferring parcels to Cadastral fabric, they can be edited or even drawn as a new parcel. In addition, by entering the descriptive information of each parcel, such as owner’s name, type of use, cadastral ID, number of floors, etc., the specifications of each plot of land (parcel) can be accessed. Then, the Automatic Parcels Division tool was implemented as a tool in GIS for automatic division of land plots. This tool has the ability to automatically separate parcels based on shape, size, and direction. This tool can perform divisions visually and statistically very similar to real-world divisions.

One of the pillars of spatial planning is determining the role and function of regions based on their competitive advantages. By developing innovative infrastructure that supports production, regional competitiveness improves regional efficiency and forms a network of complementary and reciprocal relations between regions, which ultimately leads to the capital attraction, value creation, and integrated regional development. In this regard, the present study aims at identifying the creative regions of Iran and determining the competitive advantages of those regions. This study is explanatory applied in terms of purpose and descriptive-analytical in terms of methodology. Derived from the document analysis of secondary sources, quantitative data was used to analyze both creativity and competitiveness. The data used to identify creative regions included the number of scientific and research centers and companies, consulting engineers, the industries with high and medium technology, employees in the manufacturing sectors, and the university students of the provinces of Iran. Moreover, the number of employees in major activity groups was used to determine competitiveness. Factor analysis and cluster analysis were performed to identify creative regions, while correspondence analysis was performed to determine competitive advantages in SPSS software. The results show that the provinces of Tehran, East Azerbaijan, Isfahan, Khuzestan, Fars, Kerman, and Mazandaran have the knowledge infrastructure capacity required for creative regions. Isfahan’s share of construction is 11.1 percent, while Tehran’s share of information is 41.2 percent. The interpretation of angles smaller than 0.2 between correspondence analysis vectors reveals that Tehran and Isfahan provinces have competitive advantage in information and communication and manufacturing, respectively. It was also found that most regions of the Iran do not have a specialized role and the competitive advantages of the provinces are not supported by the knowledge infrastructure of the regions.