GIS-based Spatial Analysis Research Articles

Ecological degradation and non-carcinogenic health risks of potential toxic elements: a GIS-based spatial analysis for Doğancı Dam (Turkey).

This study was carried out to determine the ecological degradation and non-carcinogenic health risks at Doğancı Dam, Bursa, Turkey. Potentially toxic element (PTE) concentrations (ppm) were as follows: Fe (55.030) > Al (27.220) > Mn (1053) > Cr (181) > Ni (180) > Zn (95) > Cu (62) > As (17) > Pb (11) > Cd (0.20) > Hg (0.108). As, Pb, Cd, and Hg were enriched anthropogenically, while other PTEs were of natural origin.The contamination severity index (CSI) indicated a moderate PTE contamination in the dam, mostly due to lithogenic effects. According to the modified hazard quotient (mHQ), ecological risk was identified at the level of extreme severity for Ni of lithological origin, of high severity for Cr of considerable severity for As of anthropogenic origin, and of moderate severity for Cu. According to the ecological contamination index (ECI), the dam had an ecological risk of a slight-to-moderate contamination. Health risk index showed no non-carcinogenic health risks in the dam. Mining, highways, and agricultural activities were identified as the primary anthropogenic drivers to be monitored. The ongoing anthropogenic activities in the Nilüfer Stream basin and natural factors affect the ecological degradation and non-carcinogenic health risk level of the dam.

Assessing the spatiotemporal changes, associated carbon stock, and potential emissions of mangroves in Bahrain using GIS and remote sensing data

The gray mangrove (Avicennia marina (Forssk) Vierh) constructs one of Bahrain’s most critical ecosystem, severly deteriorating, which has been severely deteriorated by increasing anthropogenic pressures. This study aimed to assess the spatiotemporal changes in the mangrove habitat around Tubli Bay, Kingdom of Bahrain, over the last 50 years through achieving the following: (1) detect the progressive reduction in the mangrove cover using Geographic Information Science and Systems (GISs) techniques and remote sensing data, (2) estimate the changes of above–below ground (AGB-BGB) carbon sequestered in the mangroves using a GIS-based spatial analysis approach, and (3) estimate the potential carbon emission changes from the loss of original mangrove habitats. Various GIS and remotely sensed data were employed in the study, including high-resolution satellite images from Worldview-3, Worldview-2, IKONOS, and QuickBird, coupled with true-color orthorectified aerial photographs. Additional data was acquired from fieldwork and the ancillary GIS maps. Image processing of the satellite data was conducted using ENVI 5.5 Software. ArcGIS 10.8 was used for digitizing the mangrove areas in all satellite imagery. The final maps were used to calculate mangrove area changes and carbon loss due to land reclamation activities. Our results indicated that Bahrain lost more than 95% of the natural mangrove cover from 1967 to 2020. The mangrove extent area’s net loss reached 280 ha during 1967–2020, as it declined from 328 ha in 1967 to 48 ha in 2020. The primary cause of the decline was land reclamation associated with urban development. The rates and causes of the loss varied both spatially and temporally. Due to land clearing, the total carbon stored in the mangrove habitats declined from 34,932.2 Mg C ha−1 in 1967 to 5112 Mg C ha−1 in 2020. Consequently, the potential carbon Sequestration decreased from 128,200.44 Mg CO2e ha−1 in 1967 to 18,761.04 Mg CO2e ha−1 in 2020. Our study urges for more efficient conservation of the remaining mangroves in Bahrain to sustain their valuable ecosystem services particularly carbon sequestration.

Association analysis on spatiotemporal characteristics of block-scale urban thermal environments based on a field mobile survey in Guangzhou, China

City blocks typically exhibit diverse thermal environmental characteristics due to their self-character complex spatial morphology and lateral thermal interactions from neighboring blocks. A field survey illustrating the spatiotemporal characteristics of block-scale thermal environments is urgently required to explore the inner-associated effects. A mobile survey method was conducted with two selected typical case regions during summer in Guangzhou, China. A block-based local climate zone (LCZ) mapping method was applied to the case regions, and diversified LCZ types that coexist in irregular states were obtained. Then, a multi-step data processing method integrating temporal correction and GIS-based spatial analysis was implemented to obtain the spatiotemporal distribution atlas of air temperature in LCZ blocks. Throughout this study, the air temperatures displayed time-varying characteristics and changed significantly with LCZ types. The correlation analysis showed that the sky view factor and pervious surface fraction present significant positive effects on block-scale air temperature during the daytime. Next, a spatial heterogeneity analysis conducted by fitted semivariogram curves at different times revealed the spatial variability of the urban thermal environment. Additionally, further spatial autocorrelation analysis of thermal interactions between blocks resulted in significant spatial cluster characteristics of air temperatures in the case regions.

GIS-Based Spatial Correlation Analysis: Sustainable Development and Two Generations of Demographic Changes

Population growth is a global issue that contributes to the changes in the distribution and concentration of population. Population growth affects the sustainable development of an area from both a social and spatial point of view. To relate the global problem to a local issue, this research investigates one of the Malaysian government policies addressed as the New Economic Policy (NEP) because the policy may be linked to long-term spatial demographic changes in Peninsular Malaysia, particularly in the distribution of people. Back in 1970, the policy was implemented after an unwanted incident on 13 May 1969. Its goals were to eradicate poverty regardless of race and to restructure society by eliminating the identification of race with economic functions. To measure the successfulness of the policy, two indicators that were derived from the goals are the long-term spatial changes of both racial and occupational segregation. The magnitude for both segregations was calculated using the Entropy Index (H). The values were then carried forward to evaluate the relationship between these two variables. The final analysis was conducted using the Local Bivariate Relationships application of a Geographic Information System (GIS) tool. The outputs then reflect the two sustainable goals that are, (i) reduced inequalities, and (ii) sustainable cities and communities in Peninsular Malaysia.

Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era.

The research of environmental geochemistry entered the big data era. Environmental big data is a kind of new method and thought, which brings both opportunities and challenges to GIS-based spatial analysis in geochemical studies. However, big data research in environmental geochemistry is still in its preliminary stage, and what practical problems can be solved still remain unclear. This short review paper briefly discusses the main problems and solutions of spatial analysis related to the big data in environmental geochemistry, with a focus on the development and applications of conventional GIS-based approaches as well as advanced spatial machine learning techniques. The topics discussed include probability distribution and data transformation, spatial structures and patterns, correlation and spatial relationships, data visualisation, spatial prediction, background and threshold, hot spots and spatial outliers as well as distinction of natural and anthropogenic factors. It is highlighted that the integration of spatial analysis on the GIS platform provides effective solutions to revealing the hidden spatial patterns and spatially varying relationships in environmental geochemistry, demonstrated by an example of cadmium concentrations in the topsoil of Northern Ireland through hot spot analysis. In the big data era, further studies should be more inclined to the integration and application of spatial machine learning techniques, as well as investigation on the temporal trends of environmental geochemical features.

Agropastoral landscapes at the Andean region of Northern Calchaquí Valley (Salta, Argentina): An archaeological and anthropological analysis

Traditionally, in archaeological and anthropological literature, the environment of the Andean valleys of northwest Argentina has been considered a mainly arable agricultural region, in contrast to the environment of Puna where the traditional way of life was generally associated with pastoral activities and seasonal mobility. However, new evidence suggests greater importance for animal grazing and associated mobility strategies strongly connected with arable agriculture in the valleys. This work is framed as an ethno-archaeological research project that aims to explore multiple dimensions of practices involving animal management and its relevance to the way of life in the Northern Calchaquí Valley (Salta, Argentina) and how they contribute to shape archaeological landscape. A mixed methodological strategy was applied to construct a contemporary model providing analytical tools for the study of archaeological landscape. This model looks at an agropastoral way of life in this region since pre-Hispanic times, on the basis of a strategy that combines ethnographic information and GIS-based spatial analysis, supported by archaeological evidence. Is expected that this work could make a contribution to the interpretation of the archaeological record and the study pre-Hispanic landscapes in the area, through a greater understanding of the local way of life, its spatial dimension and its temporal depth.

The Harvesting Memories Project: Historical ecology and landscape changes of the Sicani Mountains in Sicily

The Harvesting Memories project aims to investigate the historical landscape dynamics in an inner area of the Sicani Mountains district in Western Sicily (Contrada Castro, Corleone-Palermo). The interdisciplinary approach of the project allowed us to combine and integrate methods from different disciplines such as historical ecology, landscape archaeology, archaeobotany and GIS-based spatial analysis. In this paper some results have been summarized. The comparison between land mosaic change during the last 60 years, the relationship between site catchment area and land suitability and the correlation between archaeobotanical and phytosociological data. This approach underlined the relevance of the historical ecology for understanding landscape trajectories and planning strategy of suitable development of rural areas.

Associations between Wildfire Risk and Socio-Economic-Demographic Characteristics Using GIS Technology

Wildfire is a natural hazard caused mostly by the interaction of human systems and natural phenomena. This research aims to investigate how extreme wildfire events and disasters that occurred in California in the recent three decades are related to socio-economic-demographic characteristics at the levels of census tracts and counties. In addition, this research will use the data of historic wildfires to show counties and census tracts vulnerable to the natural hazard as well as the cyclical changes such as seasonal and annual fluctuations in the wildfire occurrences in the state. To decide how those variables correlate, this research used a Geographic Information System (GIS) designed to collect, analyze, query, and display geographical information. Two types of secondary data were used to conduct the research. One is the geospatial data showing each location of wildfires. The other is the data about such sociodemographic characteristics as race, ethnicity, level of education, and income, which can be collected through the Bureau of Census. In particular, the research employing GIS-based spatial analysis created maps that represent information on the geographic locations of the wildfires at the different geographic levels as well as demographic and socioeconomic factors influenced by the potential risk of wildfires. There are several researching findings. First, this research showed the wildfire-prone communities have comparatively higher level of representation for the populations such as the White and Native Americans. Second, it reveals that Asian people would prefer to reside in communities with a lower level of wildfire risk. In contrast with previous research reporting the Black, Hispanic or Native American people are more vulnerable to wildfire, this research showed only the census tracts with the higher number of the Native Americans are more exposed to the wildfire risk, compared with other census tracts. Third, it revealed that people with a higher level of educational attainment would prefer to reside in communities with a lower level of chemical risk. Forth and lastly, this research indicates that the census tracts that have a higher median household income and median housing price have a negative relationship with the wildfire risk, meaning that people with a higher level of the income or a relatively higher-priced home prefer residing in communities less subject to the natural hazard. Therefore, it can be concluded that associations exist between wildfire risk and certain socio-economic and demographic characteristics.

A GIS-Based Spatial Variations Analysis of Water Quality in Domestic Wells in The Buttala Area, Sri Lanka

Water quality can be identified as the main agent of human health. Different approaches and techniques can be used to analyze groundwater quality. Among them, Geographical Information Systems (GIS) is one of the approaches that can be utilized to analyze water quality levels in a spatial context and it provides more powerful tools that can be interpreted the spatial variation of the water quality parameters over the geographical space.Accordingly,the study was conducted to analyze spatial variations of the water quality in drinking wells in the Buttala Divisional Secretariat (DSD) which is an agricultural area in the dry zone.The methodological procedure is extremely important in this type of research thus, the spatial Multi-Criteria Decision Analysis (MCDA) procedure was used. Multi-criteria analysis has been widely applied to solve decision-making problems related to the environment, and natural resource management. In the study, Analytic Hierarchy Process (AHP) and MCDA integrated approach were used to visualize the spatial patterns of the water quality in the study area and to formulate the water quality index map.To generate the water quality index map, pH value, turbidity, electrical conductivity (EC), florid, and nitrate of 48 water samples were collected from the domestic well and tube wells from 10 Grama Niladari Divisions (GND) in Buttala DS.The ArcGIS 10.3 software was used for spatial analysis like Inverse Distance Weighting (IDW) and kriging.The results revealed that 11.2% of the water of the drinking wells in the study area has a good fitness level, while 68.7% of wells show a moderate quality level. Out of the total samples is shown that 18.5% of wells had a poor-quality level, and 1.2%was found to be very poor for drinking purposes. And also, 0.4 % of the area is unsuitablefordrinking.The study shows that geospatial techniques are more effective for studying the water quality in any area in an accurate decision support tool.

Using GIS-based spatial analysis to determine urban greenspace accessibility for different racial groups in the backdrop of COVID-19: a case study of four US cities.

As the United States leads COVID-19 cases on global charts, its spatial distribution pattern offers a unique opportunity for studying the social and ecological factors that contribute to the pandemic’s scale and size. We use a GIS-data-based approach to evaluate four American cities—Anchorage (Alaska), Atlanta (Georgia), Phoenix (Arizona), and Portland (Oregon) characterized by the significant composition of different racial and ethnic group populations. Building upon previous studies that investigated urban spatial inequalities using the environmental justice framework, we examine: (1) the relative racial vulnerability of Census Block Groups (CBG) and ZIP Code Tabulation Areas (ZCTA) to COVID-19 (2) green space distribution at CBG and ZCTA scale. Using standard normalization methods, we ranked racial vulnerability against % available green space for each city. Our results highlight the legacy of past and present urban planning injustices. The project is useful from environmental justice, public health management, and urban planning perspectives.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10708-021-10538-8.

Spatial Variability of Groundwater Quality for Freshwater Production in a Semi-Arid Area

Presenting groundwater quality assessment for different usages using one index is helpful to monitor the quality of this invaluable resource and reduce the cost of freshwater production, particularly in arid and semi-arid regions. The drinking groundwater quality index (DGWQI) is one the best indicators for groundwater quality assessment. Therefore, the purpose of the present research was to assess and map the groundwater quality of an aquifer for freshwater production in a semi-arid region, using GIS-based spatial analysis of DGWQI. For this goal, mean data from 70 wells collected during 2010–2018 were used. Results showed that total dissolved solids (TDS), electrical conductivity (EC), and total hardness (TH) had the highest impact on groundwater quality that exceed the permissible range for drinking purposes. Results also revealed that 42% of samples had a DGWQI value between 0 and 100 (appropriate quality class). Sensitivity analysis determined that Mg2+, EC, and TDS with highest mean variation indexes of 18.98, 20.68, and 19.04, respectively, were the most sensitive parameters in the calculation of DGWQI. According to R2 and RMSE, the ordinary kriging and spherical semi-variogram model had good performance for spatial analysis for all DGWQI, Mg2+, EC, and TDS. The DGWQI map showed that in the southern parts the groundwater (50% of the area) had unsuitable quality for drinking.

Multi-dimensional earthquake vulnerability assessment of residential neighborhoods of Mymensingh City, Bangladesh: A spatial multi-criteria analysis based approach

The assessment of the vulnerability of a city to different natural hazards is the prerequisite to achieving sustainable urban resilience. The city of Mymensingh is located in the most earthquake vulnerable zone of Bangladesh and surrounded by multiple fault lines including the Madhupur Blind Fault, Dauki Fault, and Sylhet-Assam Fault lines. The city also achieved the title of the 8th divisional city in 2015, thereby opening the door to unrestrained future development. Therefore, it is of paramount importance to assess the earthquake vulnerabilities of Mymensingh city and this research is envisioned to evaluate this vulnerability by combining 23 parameters derived from four major dimensions-geological, socio-economic, structural and systematic dimensions. The MCDM techniques of Analytical Hierarchy Process and Weighted Linear Combination coupled with GIS-based spatial analysis were applied in this study to assess the residential neighborhood-level earthquake vulnerability. The findings show that out of the 241 residential neighborhoods of Mymensingh city, 51 are observed to be highly vulnerable, while 123 are exposed to medium vulnerability and 67 to low vulnerability. According to the analysis, the topmost factors that make Mymensingh City highly earthquake-vulnerable are the soil type, proximity to the fire station, elderly population levels, Peak Ground Acceleration, child population levels and distance to healthcare facilities. The simplistic, efficient, and reasonably accurate methodology and findings of this research are expected to be helpful for policymakers in low-income countries to prioritize special consideration areas, resource targeting, and deciding on appropriate planning and development control interventions for disaster management.

Impact of the Kunming–Bangkok Highway on Land Use Changes along the Route between Laos and Thailand

Road construction fragments the landscape, reduces connectivity, and drives land use changes. To our knowledge, little is known about the scope and intensity of the effects of cross-border roads on changes in land use. Here, with the land use data products provided by the US Agency for International Development’s SERVIR Mekong project, using the GIS-based spatial analysis to quantitatively analyze and compare the effects of the cross-border road on land use changes within a 30 km buffer area along the Kunming–Bangkok Highway between Laos and Thailand. The results show the following: The greater the distance was from the highway, the smaller were the overall changes in land use within the buffer zone. A comparison of the situation before and after the road was opened in 2013 revealed significant differences in the most influential land use types of agricultural expansion, i.e., from 47.07% to 52.07% (the buffer zone was 1 km). In particular, 57.32% (1381.93 ha) and 40.08% (966.46 ha) of the land occupied by forests had been converted into land for plantation and agriculture, respectively, from 2013 to 2018. The scope of the impact of the operational route on the dynamics of land use was inconsistent. The largest impact before the road became operational was within 4 km of the buffer zone (0.26 to 0.24). Once the road had been opened, the range of its impact was beyond 10 km (0.63 to 0.57). The work here can provide a scientific basis for regional transportation planning and the sustainable use of land resources.

GIS as an Epidemiological Tool to Monitor the Spatial-Temporal Distribution of Tuberculosis in Large Game in a High-Risk Area in Portugal.

Simple SummaryHunting of large game is an activity of great social and economic importance. However, it can cause public health problems due to the zoonotic diseases of these animals, such as tuberculosis. Regular space–time monitoring of hunted animals’ health status allows both the hunters and competent authorities to understand the geographic location of the hunted animals, as well as the occurrence of possible diseases in these animals. This investigation presents the results of the assessment of the spatial–temporal distribution of tuberculosis in large game in a tuberculosis high-risk area in Portugal in the form of maps, which allow any interested party to quickly analyse the hunting situation regardless of their technical or scientific knowledge.Since April 2011, Portugal has implemented specific national legislation (Notice No. 1/2011), defining “Epidemiologic Risk Areas for Bovine Tuberculosis in Large Game” and mitigation measures in these areas, including Idanha-a-Nova county. A GIS project was created to record information that would allow us to analyse the spatial–temporal distribution, both for hunting bags and tuberculosis occurrence, in hunted wild boar and red deer in Idanha-a-Nova. Hunting bag and tuberculosis-like lesion data were recorded during post-mortem inspection across 11 hunting seasons, totalling 9844 animals. The difference in tuberculosis occurrence for these species was statistically significant in nearly all 11 seasons, with wild boars presenting approximately twice the occurrence of red deer. No significant difference was noted before and after the Notice No. 1/2011 implementation. These results, following GIS-based spatial analysis, enable us to state that both large game species displayed an irregular tuberculosis pattern for the 2006–2016 period, and we identified some specific areas of high risk for both species. Southern areas of the county may be considered the priority for intervention. This research demonstrates the potential of GIS tools to evaluate, in the field, the results and efficacy of legislation such as Notice No. 1/2011, and to ensure the correct implementation of cost-effective mitigation strategies for tuberculosis in large game species.

SPATIAL ANALYSIS OF GANDONG RIVER WATER POLLUTION FROM LEATHER INDUSTRY AT MAGETAN DISTRICT

Pollution of Gandong River occurs due to the disposal of waste water from the leather industry into the river body which still exceeds the quality standard even though it has gone through the neutralization stage. Organic substances from the tanning process that decompose can release gases such as sulfide or ammonia, causing unpleasant odors. The purpose of this study was to spatially map the distribution of BOD and COD levels in the Gandong River, Magetan Regency. This study used a descriptive method with a spatial approach. The data obtained from the results of laboratory tests will be analyzed comparatively to quality standards. Then were analyzed using GIS-based spatial analysis (Geographical Information System) using ArcMap 10.3 software. The results of research conducted on river water from the upstream, middle and downstream rivers have parameters that exceed the quality standard according to the Government of the Republic of Indonesia Regulation Number 22 of 2021, namely the BOD parameter at point 2 with a value of 48.31 mg/l and at point 2 3 which is 45.43 mg/l. Meanwhile, the COD levels in the industry showed results of 184.01 mg/l and BOD levels of 75.37 mg/l so that they still exceeded the quality standard in accordance with the Regulation of the Minister of Environment and Forestry Number P.16 of 2019.

Mesoscale structures in the Rough Creek Graben, U.S. Midcontinent craton: An integrated DEM analysis, structural modeling, and field-based approach

The Rough Creek Graben (RCG) within the U.S. Midcontinent craton possesses some of the world's longest cave and karst landforms such as the Mammoth Cave. Despite the graben's position at the junction of two active seismic zones, the New Madrid Seismic Zone (NMSZ) and the Wabash Valley Seismic Zone (WVSZ), the RCG is seismically inactive. Here, we map mesoscale geologic structures within the RCG for understanding structural deformation and styles. We integrated DEM (Digital Elevation Model)-based structural analysis, field-based structural documentation, 3-d models, and GIS-based spatial analysis. Results of this integrated study reveal the presence of dominantly E-W-striking mesoscale structures with secondary NW-SE- and NE-SW-strikes. Many of these structures are documented in the field as high-angle normal faults with distinct fault cores, displacements, and damage zones. Most of the mapped structures are likely originated from extensional stresses associated with the development of the RCG. The presence of less frequent contractional structures (reverse and thrust faults, and folds) suggests subsequent strain localization within this graben. Although most of these structures are seismically inactive, understanding their deformation and kinematics is crucial for synthesizing any reactivation in response to a large magnitude earthquake originating from either the NMSZ or the WVSZ. The findings of this study not only advance our knowledge of the mesoscale structures in a midcontinent cratonic rift but also help evaluate potential earthquake hazards and risks. Moreover, positive correlations between these mesoscale structures and the karst systems suggest a relationship between these structures and the ultimate morphology of conduit systems that assisted in developing these karst landforms.

Place-value Attachment on Provisional and Cultural Services for Sustainable Management of Ngezi Forest

This paper uncovers the importance of people’s place-values on sustainable forest management, and how such values can be incorporated into forest management actions and decision-making. Specifically, it focuses on mapping economic and cultural values on forest ecosystem services; assesses how non-materials and materials benefit from forest ecosystem cause landscape fragmentation; and how this information could assist in better forest planning and management. The data were collected from ten villages surrounding the Ngezi forest reserve in Pemba, Tanzania. Data were collected through participatory mapping, field observation, and focus group discussions. A map of place-values for each respondent was transferred from paper to digital format, digitized and coded using the GIS, and analysed using kernel density. Non-spatial data were processed and integrated into GIS-based spatial analysis. The results indicate that only 12 areas were identified as very high-valued and these require careful consideration for sustainable forest planning and management. About 4 out of 6 very high-valued areas for material services are found inside the reserve. The areas outside the reserve are undervalued and not utilized effectively for material services. Contrary to cultural services, only 1 out of 6 very high-valued places is located inside the reserve. Furthermore, economic situations, together with social driving forces, have been important determinants of forest values in the areas. Therefore, placevalues issues, particularly economic development outcomes, preservation of the aesthetics and improvement of recreational amenities should be considered when examining sustainable forest resource management.

GIS-based spatial autocorrelation analysis and use of aerial photos metrics for land price per plot: a case study of Tembalang sub-district

Tembalang sub-district is included in the suburban area of Semarang which is growing very rapidly in the housing market growth sector. Research on housing policies and existing land market value mechanisms was carried out by empirical analysis using traditional econometric models with multiple regression analysis and GIS-based spatial autocorrelation models. This research was conducted by modeling land prices based on land parcels from the interpolation of land price samples from the field survey results as well as analyzing the level of data closeness to the Tax Object Selling Value (NJOP). Land parcel data was created using the 2018 metric aerial photograph (10 cm spatial resolution). The dependent variable in the regression analysis model used data on the average value of land prices for each zone in 2018. The variables used in the first modeling, namely the geographic location, transportation accessibility, trade center and service intensity were used as independent variables. The apply Radial Basis Function (RBF), spatial autocorrelation model, in integration and comparative analysis of models with a focus on the analysis of factors affecting land prices, especially heterogeneity of spatial characters.

A GIS-Based Spatial Analysis Model Approach for Identification of Optimal Hydrotechnical Solutions for Gully Erosion Stabilization. Case Study

The accentuated degradation of agricultural lands as a result of deep erosion processes is the main problem identified in abandoned agricultural lands under the rainfall intensities, increasing number of hot days, indirectly under the impact processes derived from them (soil erosion, vegetation drying, etc.), as well as inadequate or poor management policies implemented by local authorities. The present study aims to develop and present a methodology based on GIS spatial analysis to choose the best hydro-amelioration solution for the arrangement of a complex ravine that negatively affects the entire agroecological area in its immediate vicinity. The proposed model is developed on spatial databases obtained based on UAV flights, the simulation of flow rate values and the establishment of three hydraulic analysis models through the HEC-RAS software with the main purpose of evaluating the results and databases, in order to identify the best implementing model for the stabilization and reduction in erosion within the analysed area. The comparative analysis of the three analysed scenarios highlighted the fact that a dam-type structure with overflow represents the best hydro-ameliorative solution to be implemented in the present study. The accuracy of the obtained results highlights the usefulness of developing GIS models of transdisciplinary spatial analysis to identify optimal solutions that can be implemented in territories with similar characteristics.

Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

With the rapid development of electric vehicles (EVs) around the world, debates have arisen with regard to their impacts on people’s lifestyles and urban space. Mining spatio-temporal patterns from increasingly smart city sensors and personal mobile devices have become an important approach in understanding the interaction between human activity and urban space. In this study, we used location-based service data to identify EV owners and capture the distribution of home and charging stations. The research goal was to investigate that how the urban form in regions under rapid urbanization is driven by EV use, from a geographical perspective. Using a case study of the expanding metropolis of Beijing, GIS-based spatial statistical analysis was conducted to characterize the spatial-pattern of the homes of EV owners as well as their charging preferences. Our results indicate that the spatial clustering of the homes of EV owners in non-urban central areas—suburban areas—is significantly higher than that in urban central areas. According to the records of visits to charging stations, the spatial interaction distance between the dwellings of EV owners and their visits to charging stations exhibits significant distance attenuation characteristics. 88% of EV owners in this research travels within 40 km (Euclidean distance) between housing and charging stations. At the same time, there were significant differences in the spatial patterns between working days and non-working days which are affected by commuting activities. The three types of urban spatial interaction patterns were identified and categorized by visualization. This transformation to EV use in the city influences several aspects of people’s decisions and behaviors in life. Understanding the impacts will provide valuable information for the development of EVs and their implications in the electrification of transportation, smart planning, and sustainable urbanization.