The geographical distribution characteristics of villages characterised by ethnic minorities are determined by the selection of the site when the village was initially established. The location of inherited and well-preserved minority villages must be exceptionally compatible with the natural terrain, with a logical relationship. Nonetheless, the issue of village location, which is directly related to the development of the features of the geographical distribution, has received little attention from scholars. The average nearest proximity index, Voronoi, kernel density analysis, proximity analysis, and the Geographical Detector (GeoDetector) were used to analyse the geographic distribution characteristics of villages and their correlation with terrain, as well as the difference between the influence of each terrain factor. The findings indicated the following. (1) The geographical distribution of minority villages in Fujian Province is of the agglomeration type, with a significant “mononuclear” feature, and the topography has a facilitating effect on the clustering distribution of villages. (2) The geographical distribution of minority villages in each city of Fujian Province coexisted with the agglomeration type and the dispersion type, and the role of topography in promoting the agglomeration-type distribution of villages was not affected by the distribution density of villages. (3) The site selection of Fujian-minority villages is characterised by medium altitude, moderate slope, sun exposure, and no obvious hydrophilicity. Minority villages are mainly located in areas with an elevation of 202–647 m; a slope of 6–15°; a flat land aspect with a south slope, southeast slope, or southwest slope; and distance of 500–1500 m from 5–20 m wide rivers of level 2. (4) The site selection of Fujian minority villages is influenced by various topographic factors, such as elevation, slope, aspect, river buffer, river width, and river level, among which river width has the most substantial effect. (5) All topographic factors have a two-factor enhancing relationship with each other, aspect and slope have the most substantial effect and play a dominant role in site selection. The research findings illuminate the internal logic of the geographical distribution differentiation of villages characterised by ethnic minorities, which is critical for promoting the protection of modern ethnic-minority villages.

With the improvement of technologies, people's demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.Future Internet 2020, 12, 30 2 of 18 prediction, and the error is relatively large. In recent years, the development trends of computer science, applied mathematics, statistics, and semiconductor hardware have brought iterative progress in artificial intelligence technology. This makes the simulation, analysis, prediction. Literature ReviewBig data mainly refers to data management on a certain scale. Due to the increase of the data volume, speed, and type, traditional methods cannot be used to proceed [1]. The artificial intelligence technology represented by machine learning and deep learning algorithms has a great dependence on datasets. Only when the original data is large enough can the trained algorithm be more accurate. Therefore, before applying the model, it is necessary to obtain enough data for repeated training.In the traditional building with related planning and landscape fields, big data was firstly applied to Geographic Information System (GIS), including a variety of spatial information such as land conditions, terrain, and climate. As for the single building, its automated development is mainly reflected in the field of intelligent building, with small but higher accuracy. The complexity of intelligent building systems is high. Meanwhile, the physical structure, indoor environment, and operating systems of the entire building are monitored and analyzed. It can develop the building in the direction of intelligence. With the help of building big data system, it can analyze the use of the building in real-time, integrate the related information of the surroundings, and let the building make optimal adjustments according to the actual situation [2]. In the process of optimal adjustment, IoT sensors and controllers are usually targeted in the building. The organic combination of physical devices and computer algorithms pla...

This study aimed to assess the compositions and configurations of the urban green spaces (UGS) in urban functional land use areas in Addis Ababa, Ethiopia. The UGS data were extracted from Landsat 8 (OLI/TIRS) imagery and examined along with ancillary data. The results showed that the high-density mixed residence, medium-density mixed residence, and low-density mixed residence areas contained 16.7%, 8.7%, and 42.6% of the UGS, respectively, and together occupied 67.5% of the total UGS in the study area. Manufacturing and storage, social services, transport, administration, municipal function, and commercial areas contained 11.6%, 8.2%, 6.6%, 3.3%, 1.3%, and 1% of the UGS, respectively, together account for only 32% of the total UGS, indicating that two-third of the UGS were found in residential areas. Further, the results showed that 86.2% of individual UGS measured less than 3000 m2, while 13.8% were greater than 3000 m2, demonstrating a high level of fragmentation. The results also showed that there were strong correlations among landscape metrics, while the relationship between urban form and landscape metrics was moderate. Finally, more studies need to be conducted on the spatial pattern characteristics of UGS using very high-resolution (VHR) images. Additionally, future urban planning, design, and management need to be guided by an understanding of the composition and configuration of the UGS.

Addis Ababa, the capital of Ethiopia, is urbanizing very fast. This study aimed to assess urban expansion and Urban Green Spaces (UGS) change in the city from 1989 to 2019. Remote Sensing and Geographical Information System (GIS) and Landscape Expansion Index (LEI) were used to extract Land Use Land Cover (LULC) data, measure urban expansion and UGS change and analyze urban growth pattern in inner zone, outer zone and eight quadrants. The results showed that urban area in the inner zone increased from 3712 ha to 3716 ha (0.1%), and from 3716 ha to 3874 ha (4.2%) and in the first (1989–1999) and second periods (1999–2009), while it decreased from 3874 ha to 3733 ha (3.6%) in the third period (2009–2019), portraying a non-unidirectional trend of change. Conversely, the UGS in the inner zone decreased from 60 ha to 54 ha (10%), and from 54 ha to 38 ha (29.6%) in the first and second periods, while it increased from 38 ha to 53 ha (39.4%) in the third period, reporting spatial tradeoff between the two land cover types. Meanwhile, urban areas in the outer zone increased from 10,729 ha to 15,112 ha (40%), from 15,112 ha to 21,377 ha (41.4%) and from 21,377 ha to 28,176 ha (31.8%) in the first, second and third periods, respectively, representing frontiers of suburbanization. On the other hand, the UGS in the outer zone decreased from 3624 ha to 3171 ha, from 3127 ha to 2555 ha and from 2555 ha to 1879 ha, with an annual rate of decline of 1.25%, 1.8% and 2.6%, respectively, showing increasing trend of UGS destruction for urban construction. Furthermore, the LEI analysis result showed that urban expansion pattern demonstrated largely an outlying growth characterized by differentiation and isolation of patches, whereas the infill and edge expansion pattern were insignificant and fluctuated over 30 years. Furthermore, the directional analysis showed that urban area predominately expanded in SEE,> SSE,> SSW,> SWW,> and NEE directions with varying magnitude in the first, second and third period, but decreased in third period in NWW, < NNW< and NNE directions. In response to such urban growth pattern, the center of gravity of urban area shifted from north to south during the study period, displaying main direction urbanization in recent years. Conclusively, zonal and directional studies are more effective in characterizing the Spatio-temporal dynamics variabilities of urban expansion and UGS change for informed urban planning towards sustainable urban development.

Ethnic minority villages are important resources for the economy and social development of ethnic minority areas because they preserve ethnic minorities’ culture. With the rapid development of industrialization and urbanization in China, the factors affecting the development of villages have changed. With the help and guidance of the government, the gap between villages has increased. According to the development conditions of ethnic minority villages at the present stage, the suitability of their spatial distribution has been studied, the existing problems in the current development have been explored, and the development laws and future development trends have been found. To make the evaluation results more scientific and objective, Geographical Detector (Geodetector) and Absorbent Hygiene Product (AHP) methods are used to establish the evaluation model. Taking 567 ethnic minority villages in Fujian Province as the research object, 13 factors are selected from the aspects of natural geographical, socio-economy and cultural life to construct the evaluation indicator system of Fujian ethnic minority villages, and the spatial distribution suitability of Fujian ethnic minority villages is quantitatively evaluated. The findings indicated the following: (1) The per capita income of villages has the most important impact on the suitability of spatial distribution of Fujian minority villages. Through comprehensive evaluation, the impact of cultural life indicators on the suitability of the spatial distribution of the village is greater than that of socio-economic indicators and natural geographical indicators. The intensity relationship is 9:7:10. (2) The high suitability value is concentrated in Fujian Province’s southeast coastal and central areas, gradually decreasing from east to west. In Fujian Province, 82.84%of the land is suitable for the development of ethnic minority villages, with 89% of ethnic minority villages. The unsuitable areas are mostly in Fujian’s north and west. (3) The most suitable cities for the number of ethnic minority villages are Ningde City and Quanzhou City because ethnic minority villages in these two cities are mostly distributed in areas relatively close to the central urban area, with good economic conditions, flat terrain, and easy transportation. The cities of Nanping and Sanming are the least suitable for many ethnic minority villages, which are primarily limited by topographic conditions, have a backward economy, a sparse road network, and have experienced significant population loss. In the context of urbanization, the evaluation results can provide a reference for the precise development and protection of minority villages. Governments at all levels in Fujian Province can adjust and optimize the development strategies of minority villages according to the evaluation results.