Journal of Sustainable Urban & Regional Development Studies (JSURDS)

Abstract Background and Objective: Despite the widespread implementation of road traffic restrictions in Iran during the COVID-19 pandemic, there is little quantitative evidence regarding the spatial effectiveness of these policies at the intra-provincial scale. The present study aimed to analyze the impact of traffic restrictions on the spatiotemporal dynamics of COVID-19 in Kurdistan Province and to test five quantitative hypotheses.
Methodology: In this study, a balanced panel of 10 counties of Kurdistan Province was constructed for the period 2019–2022, and spatial autocorrelation was examined using the Global Moran's I index. A spatial lag panel model with random effects and a K-nearest neighbor weight matrix was estimated. The policy variable (the proportion of months with travel bans) was extracted from the announcements of the National COVID-19 Taskforce and entered into the model in the form of interaction terms with traffic and migration variables. Direct, indirect, and total effects were calculated through matrix inversion (I − λW)⁻¹, and the robustness of the results was assessed using six different spatial weight matrices.
Results and Findings: The Global Moran's I index was non-significant for all years (p-value > 0.05); however, the spatial lag model revealed a strong and significant spatial autoregressive coefficient (λ = 0.812, p < 0.001) (confirming H2). The coefficient for "bus travel" was positive and significant (β = 0.100, p = 0.004), whereas the coefficient for "private car travel", contrary to expectations, was negative and significant (β = -0.0073, p < 0.001) (partially confirming H1). The interaction terms of restrictions with car and bus traffic were nonsignificant (rejecting H3). The "restrictions × migration" interaction was positive and significant (β = 0.271, p = 0.045) (confirming H4). Hypothesis H5 was not tested due to the absence of daily mortality data. For all variables, spillover effects outweighed direct effects (for main road density: direct effect = 2.275 versus indirect effect = 5.706). The results remained robust across the six weight matrices. Hence, uniform road traffic restrictions, in the absence of essential travel management, shifted the disease transmission pathway from public travel to exempted migrations. The strong spatial autocorrelation and the predominance of spillover effects necessitate the design of regional and coordinated inter-county interventions. Policy evaluation in regions with a small number of spatial units requires advanced spatial models, and simple tests such as Moran's I are insufficient.

Abstract Background and Objective: In recent years, climate change and human activities have increasingly intensified the global water scarcity crisis. These changes have disrupted the hydrological cycle, placing surface water resources under serious threat in terms of accessibility, quality, and sustainability.
Methodology: To assess the impact of climate change on surface water resources in the Babolrood watershed, meteorological and hydrometric data were initially collected. After addressing statistical deficiencies, removing outliers, and selecting a common temporal baseline, future climate variables (precipitation, minimum temperature, and maximum temperature) were projected for the period 2020–2100 using the CanESM5 climate model under IPCC AR6 scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 within the SDSM framework. Streamflow simulation for the future period was conducted using downscaled data processed through an Artificial Neural Network (ANN). Finally, to identify trends in the projected data, non-parametric Mann–Kendall and Sen’s slope estimator tests were applied using the R software environment.
Results and Findings: Trend analysis of streamflow using the Mann–Kendall test, Sen’s slope estimator, and the ANN model over the period 2021–2100 revealed a weak and statistically insignificant decreasing trend across all SSP climate scenarios. The most pronounced decline was observed under the SSP5-8.5 scenario. Minimum temperature exhibited a non-significant increasing trend, potentially indicating nighttime or cold-season warming, while precipitation showed no discernible trend. The ANN model results were consistent with the statistical tests, confirming a gradual reduction in streamflow, thereby underscoring the need for sustainable water resource management in the face of climate change. These findings not only confirm the direct impact of climate change on surface water resources but also highlight the importance of employing intelligent models for long-term analysis and sustainable water resource management. Moreover, they underscore the necessity of integrated approaches and region-specific analyses in future studies.

Abstract Background and Objective: In recent years, the preparation of land cover maps through digital classification of remote sensing data has been considered as a suitable alternative for the preparation of this type of maps. Remote sensing is one of the new and efficient technologies in extracting land cover, updating maps, and detecting land cover changes. In this dirction, the aim of present study is to compare two methods of pixel-based and object-oriented classification in land cover mapping with using OLI image in Doviraj region, Ilam province using Idrisi Teerrset software.
Research Method: After the supply of related image and implementing geometric and radiometric corrections on image, we applied two classification methods to land cover mapping. To assess the accuracy of classification methods, we used of indices of overall accuracy, kappa coefficient, producer accuracy and user accuracy. The results show that the object-oriented classification method has more resolution than the pixel-based classification method.
Findings and Conclusion: The results of accuracy show that method of object-oriented in two indices of overall accuracy and kappa coefficient with (respectively) 94 percent and 0/93 percent was more accuracy then to pixel-based classification method with overall and Kappa coefficient 88 percent and 0/87 percent. The result of this study suggests that from object-oriented classification method use to production of land ucover map.

Abstract Background and Objective: In parallel with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems for the citizens. Achieving the amount, intensity and direction of development Construction from the past until now and forecasting the construction situation in the future is the first step towards the scientific and practical management of the physical development of urban construction, and planning and providing suitable solutions in order to create a balance between the spatial allocation of construction and all kinds of legal, economic and environmental considerations. The purpose of this research is to model and predict urban growth using satellite images and CA-Markov model.
Methodology: In this research, firstly, using multi-time Landsat images related to the years 1976, 2001 and 2021, land use changes were investigated, and then the spatial expansion of Gorgan city in  (2021 AD) and  (2050 AD) was predicted using the CA-Markov model. Based on the results of this research, the changes in land use and the level of land use in the area have been calculated and compared.  The geographical area of ​​this research is the city of Gorgan.
 Findings and Conclusion: The results of this research show that the largest increase in land use is related to urban land use (built land use), which increased from 6005.79 hectares in 2021 to 7141.66 hectares in 2050. Based on the results of this research, the growth of the city of Gorgan in the coming years will go towards the agricultural lands around the north, northwest and northeast.

Abstract Background and Objective: Simulation and continuous review of dynamic processes and urban growth patterns with regard to past development and its prediction in the future, for planners and proponents of natural resource conservation in setting sustainable development strategies, achieving sustainable urban development as well as better decision making. In this regard, the city of Karaj with the approach of being the center of the province as being industrial and migrating has had an impact on all the internal structure of the surrounding cities and villages. As a result, the use of hybrid modeling with a deeper and local perspective for codified planning for the sustainable development of the city of Karaj is inevitable. The aim of this study was to model the growth patterns of Karaj city using heuristic statistical preprocessing (ER), ordinary least squares regression (OLS) and spatial weighted regression model (GWR) and also to predict its development using the model. CA-Markov considering the 20-year period 1381-1400.
Research Method: For this purpose, first, effective criteria in this process were collected, analyzed and prepared from relevant organizations, and land use maps were extracted from Landsat satellite images. In the next step, the maps were validated and changes were detected. The results of change detection show that the largest increase in area occurred in the built-up areas (2893.86 hectares) and the largest decrease in area occurred in barren lands (808.02 hectares). Based on these changes and to avoid the trial and error method in selecting the best combination of input criteria to the GWR model, pre-processing was performed on the criteria using the ER and OLS methods. In the next step, considering the output of ER and OLS methods, 8 independent variables were selected as inputs to the models. Then, modeling of urban growth patterns was performed using the GWR model.
Findings and Conclusions: results showed that out of 8 variables, two variables had the same direction of impact (negative or positive) in the studied space and the other variables change their impact in the whole region. Finally, using the CA-Markov model, the land use map for 1420 was predicted. The final results showed that the CA-Markov model predicts that the highest rate of development will occur in 1420 in the eastern and northeastern parts of Karaj.

Abstract Background and Objective: Today decision-makers in dealing with drought consider the integrated management approach of crisis management and risk management simultaneously. So far, Mazandaran province has suffered a lot of damage from drought risk. Therefore, determining the level of vulnerability and type of meteorological drought risk situation in the study area, especially in the last two decades, is the main issue of this study.
Methodology: Methodology in order to determine the meteorological drought situation, the Standard Precipitation Index was used in this study. First, the monthlt rainfall statistics for 17 selected meteorological stations in the region were collected and tested for accuracy, precision and feasibility during the common period of 2000-2020. For each of the station in question the percentage of meteorological drought occurrence was determined on an annual time scale. Then the drought Hazard Index (DHI) was extracted by assigning weights and degrees for each of the different intensities. Also the vulnerability of meteorlogical drought was calculated using physical and socio-economic indicators and then the drought- vulnerable zones were determined. Finally based on the two factors of drought hazard index and vulnerability index, the risk of drought damage was calculated and then the zones at risk of meteorological drought were determined.
Results and Findings: Findings and conclusion the results of the research on drought conditions showed that the most severe droughts occurred in the region in 2000, 2008, 2012, 2014, 2016 and 2017. Based on the drought risk index, the results showed that northern part of the region and its central part with an area equivalent to 24% in the SPI index, 22% in the VCI index (vegetation condition index) and 28% in the VHI index (vegetation health index) and 20%   in TCI index (surface temperature index) the area of Mazadran province are very susceptible to the risk of meteorological drought. 35 percent of the province's area, located in the north of the province, has very high drought vulnerability. The results of this study indicate that the risk and vulnerabilities caused by meteorological drought seriously threaten Mazandaran province. Meteorological drought risk maps can be used as a suitable warning tool in the risk reduction action plan for all policmakers, managers and stakeholders of the studied region. This issue is of particular importance in planning agricultural activities and the optimal use of water resources, especially in this province where the livelihoods of agricultural operators depend on both rainfed and irrigated agriculture.