Search Results (51)

GPS tracking has made ski injury data abundant, yet few studies have mapped where incidents actually occur or how those patterns differ between skiers and snowboarders. To address this gap, we analyzed 8719 GPS-located incidents (4196 skier; 4523 snowboarder) spanning four seasons (2017–2022, excluding 2019–2020 due to COVID-19) at a large West Coast resort in California. Incidents were aggregated into 45 m hexagons and analyzed using Getis–Ord Gi* hot spot analysis, Local Outlier Analysis (LOA), and a space–time cube with time-series clustering. Hot spot analysis identified both activity-specific and overlapping high-injury concentrations at the 99% confidence level (p < 0.01). The LOA revealed no spatial overlap between skier and snowboarder High-High classifications (areas with high incident counts surrounded by other high-count areas) at the 95% confidence level. Temporal analysis exposed distinct patterns by activity: Time Series Clustering revealed skier incidents concentrated at holiday-sensitive locations versus stable zones, while snowboarder incidents separated into sustained high-activity versus baseline areas. These findings indicate universal safety strategies may be insufficient; targeted, activity-specific interventions may warrant investigation. The methodology provides a reproducible framework for spatial injury surveillance applicable across the ski industry. Full article

To systematically investigate the diversity and distribution patterns of Dendrocalamus in Yunnan Province, we integrated field surveys, literature reviews, specimen records, and existing research data to compile and analyze the distribution of Dendrocalamus species across the region. The results revealed the following: (1) A total of 3730 valid distribution points were compiled, representing 38 taxa of Dendrocalamus (including 32 species, 3 varieties, and 3 forms), reflecting remarkably high species diversity. These account for approximately 52% (38/73) of the global species and 69% (38/55) of those recorded in China. (2) Horizontal Distribution Pattern: In terms of distribution points, Pu’er had the highest count (929), followed by Xishuangbanna (759) and Lincang (586). Honghe, Wenshan, and Dehong also showed substantial records. Regarding species richness, Xishuangbanna ranked highest with over 20 species, while Pu’er and Honghe contained 15–20 species. Yuxi and Kunming supported 10–15 species, and Baoshan, Nujiang, Chuxiong, Wenshan, Qujing, and Zhaotong each hosted 5–10 species. In contrast, Dali, Lijiang, and Diqing recorded only 0–5 species. (3) Vertical Distribution Pattern: Distribution points were predominantly concentrated in the 1000–1500 m elevation range, whereas species richness peaked in the 500–1000 m band. Both the number of distribution points and species richness were lowest at elevations above 2500 m. (4) Based on the collected 3730 distribution points, kernel density analysis and hot spot analysis (Getis-Ord Gi*) were performed in ArcGIS 10.8. Both analyses indicated that southern Yunnan (centered on Xishuangbanna and Pu’er) exhibits significant spatial clustering characteristics, identifying it as the core distribution area for Dendrocalamus species in Yunnan Province. (5) During field surveys, a distinctive new species characterized by swollen internodes was discovered. Morphological comparison and phylogenetic analysis confirmed it as a new species of Dendrocalamus and named Dendrocalamus turgidinodis C.M.Hui, M.S.Sun & J.W.Li, it is similar to D. hamiltonii, D. fugongensis, and D. sinicus, but can be easily distinguished by culm diameter 13–16 cm, intranode swollen, culm leaf sheath deciduous, culm blade erect, culm leaf ligule ca. 5 mm tall., Foliage leaf ligule 1–1.5 mm tall (vs. 1.5–2 mm). In conclusion, this study demonstrates that Yunnan Province serves as a major distribution center for Dendrocalamus, with the genus primarily distributed from the southeastern to southwestern parts of the region, and concentrated most densely in the southern area encompassing Xishuangbanna and Pu’er. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

This study, guided by the concept hat “lucid waters and lush mountains are invaluable assets”, focuses on explicating the ecological vulnerability characteristics of the Nanpan and Beipan River Basins, a typical karst river basin in Guizhou Province. In this article, a value equivalent table was built to calculate the ecosystem service value (ESV) within the basin from 2000 to 2020. The patch landscape and urban simulation model (PLUS) was improved to forecast ecosystem changes under four scenarios in the future. The Getis-Ord Gi*statistic, a spatial analysis tool, was introduced to identify and interpret the spatial patterns of ESVs in the study area. The research indicates that: (1) from 2000 to 2020, the spatial pattern of ecosystem has significantly improved, and with a notable ESV increase in the Nanpan and Beipan River Basins, especially the fastest growth from 2005 to 2010. Forest and grassland ecosystems are the main contributors to ESV within the basin, and the spatial distribution of ESV shows a decreasing trend from southeast to northwest. (2) Under different scenarios, forest ecosystem still would have the highest contribution rate to update the ESV between 2010 and 2035. The ESV is the lowest under the cropland protection scenario, amounting to CNY 104.972 billion. Compared to other scenarios, the ESV is higher under the sustainable development scenario, reaching CNY 106.786 billion, and this scenario provides a more comprehensive and balanced perspective, relatively achieving a harmonious coexistence between humans and nature. (3) The hot spots of ESV are mainly concentrated in the southeast and along the riverbanks of the study area. Urban ecosystems are the cold spots of ESV, indicating that protecting the ecosystems along the riverbanks is crucial for ensuring the ecological security and sustainable development of karst mountainous river basins. In the future development of karst mountainous river basins, it is necessary to strengthen ecological restoration and governance, monitor soil erosion through remote sensing technology, optimize the layout of territorial space to implement the policy of green development, and promote the harmonious coexistence of humans and nature, ensuring the ecological security and sustainable development of the basins. Full article

Introduction: Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the United States (U.S.). Mississippi has the highest rate of CRC incidence in the U.S. and has large populations of black and white individuals, allowing for studies of racial disparities. Methods: We conducted an ecological study using the county as the unit of analysis. CRC incidence data at the county level for black and white populations in Mississippi, covering the years 2003 to 2020, were retrieved from the Mississippi Cancer Registry. Age-adjusted incidence rate differences and their corresponding 95% confidence intervals (CIs) were then calculated for these groups. Getis–Ord Gi* hot and cold spot analysis of CRC incidence rate racial disparities was performed using ArcGIS Pro. We used global ordinary least square regression and geographically weighted regression (MGWR version 2.2) to identify factors associated with racial differences in CRC incidence rates. Results: Age-adjusted CRC incidence rate in the black population (median = 58.12/100,000 population) and in the white population (median = 46.44/100,000 population) varied by geographical area. Statistically significant racial differences in CRC incidence rates were identified in 28 counties, all of which showed higher incidence rates among the black population compared to the white population. No hot spots were detected, indicating that there were no spatial clusters of areas with pronounced racial disparities. As a post hoc analysis, after considering multicollinearity and a directed acyclic graph, a parsimonious multiple regression model showed an association (β = 0.93, 95% CI: 0.25, 1.62) indicating that a 1% increase in food insecurity was associated with a 0.93/100,000 differential increase in the black–white CRC incidence rate. Geographically weighted regression did not reveal any local patterns in this association. Conclusions: Black–white racial disparities in CRC incidence were found in 28 counties in Mississippi. The county-level percentage of food insecurity emerged as a possible predictor of the observed black–white racial disparities in CRC incidence rates. Individual-level studies are needed to clarify whether food insecurity is a driver of these disparities or a marker of systemic disadvantage in these counties. Full article

Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer’s Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) ($\chi$² = 307.41, p < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (p < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, p < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, p < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, p < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses. Full article

With the increasing frequency of various uncertainties and disturbances faced by urban systems, urban resilience is one of the vital components of the sustainability of modern cities. An indicator system is constructed to measure the resilience levels of the Yichang–Jingzhou–Jingmen–Enshi (YJJE) urban agglomeration during 2010–2023 based on four domains—economy, ecology, society, and infrastructure. This paper analyzes the spatiotemporal differentiation of resilience in YJJE in conjunction with the entropy weight method, Getis–Ord Gi* model, and robustness testing. Then, the factor contribution model is used to discern key driving elements of urban resilience. Finally, the CA-Markov model is implemented to predict urban resilience in 2030. The results reveal that the values of resilience in YJJE increase at a rate of 3.25%/a and continue to rise, with the differences among cities narrowing over the examined period. Furthermore, the urban resilience exhibits a significant spatially heterogeneity distribution, with Xiling, Wujiagang, Xiaoting, Yidu, Zhijiang, Dianjun, Dangyang, Yuan’an, Yiling, and Duodao being the high-value agglomerations of urban resilience, and Hefeng, Jianli, Shishou, and Wufeng being the low-value agglomerations of urban resilience. The marked heterogeneity of resilience in the YJJE urban agglomeration reflects the disparity in economic progress across the study area. The total amount of urban social retail, financial expenditure per capita, GDP per capita, park green space area, urban disposable income per capita, and number of buses per 10,000 people surface as the key influencing factors in relation to urban resilience. Finally, the levels of resilience among cities within YJJE will reach the medium level or higher than medium level in 2030. Xiling, Wujiagang, Xiaoting, Zhijiang, Dianjun, Dangyang, and Yuan’an will remain significant hot spots of urban resilience, while Jianli will remain a significant cold spot. In a nutshell, this paper can provide scientific references and policy recommendations for policymakers, urban planners, and researchers on the aspects of urban resilience and sustainable city. Full article

(1) Background: Measles remains a major cause of disease and death worldwide, especially in the World Health Organization African Region. This study aimed to estimate the coverage of measles vaccinations and map the spatial distribution of measles vaccination dropout in Ethiopia; (2) Methods: A cross-sectional survey was conducted in Ethiopia’s underprivileged areas. The study included 3646 mothers/caregivers of children. ArcGIS for the spatial analysis, Global Moran’s I statistic for spatial autocorrelation, and Getis-Ord Gi* statistics for hot spot analysis were applied; (3) Results: Overall, coverages of measles-containing-vaccine first- and second-doses were 67% and 35%, respectively. Developing regions had the lowest coverages of measles-containing-vaccine first- and second-doses, 46.4% and 21.2%, respectively. On average, the measles vaccination dropout estimate was 48.3%. Refugees had the highest measles vaccination dropout estimate (56.4%). The hot spot analysis detected the highest burden of measles vaccination dropout mainly in the northeastern parts of Ethiopia, such as the Afar Region’s zones 1 and 5, the Amhara Region’s North Gondar Zone, and peripheral areas in the Benishangul Gumuz Region’s Assosa Zone; (4) Conclusions: The overall measles vaccination coverages were relatively low, and measles vaccination dropout estimates were high. Measles vaccination dropout hot spot areas were detected in the northeastern parts of Ethiopia. Full article

The vadose zone acts as a natural buffer against groundwater contamination, and thus, its attenuation capacity (AC) directly affects groundwater vulnerability to pollutants. A regression model from the previous study predicting the overall AC of soils against diesel was further expanded to the GIS-based overlay-index model. Among the six physicochemical parameters used in the regression model, saturation degree (SD) is notably susceptible to climatological and meteorological events. To accommodate the lack of soil SD historical data, a series of infiltration simulations were separately conducted using Phydrus code with moving boundary conditions (i.e., rainfall records). The temporal variation of SD and the resulting AC under transient conditions are captured by building a space–time cube using a temporal raster across the study area within the designated time frame (1997–2022). The emerging hot spot analysis (EHSA) tool, based on the Getis–Ord Gi* and Mann–Kendall statistics, is applied to further identify any existing pattern associated with both SD and AC in both space and time simultaneously. Under stationary conditions, AC decreases along depth and is relatively lower near water bodies. Similarly, AC cold spot trends also show up near water bodies under transient conditions. The result captures not only the trends across time but also shows the exact location where the changes happen. The proposed framework provides an efficient tool to look for locations that have a persistently low or a gradually decreasing ability to attenuate diesel over time, indicating the need for stricter management regulations from a long-term perspective. Full article

Rabies is endemic in South Africa and rabies cycles are maintained in both domestic and wildlife species. The significant number of canine rabies cases reported by the World Organization for Animal Health Reference Laboratory for Rabies at Onderstepoort suggests the need for increased research and mass dog vaccinations on specific targeted foci in the country. This study aimed to investigate the spatiotemporal distribution of animal rabies cases from 1998 to 2017 in northern South Africa and environmental factors associated with highly enzootic municipalities. A descriptive analysis was used to investigate temporal patterns. The Getis-Ord Gi statistical tool was used to exhibit low and high clusters. Logistic regression was used to examine the association between the predictor variables and highly enzootic municipalities. A total of 9580 specimens were submitted for rabies diagnosis between 1998 and 2022. The highest positive case rates were from companion animals (1733 cases, 59.71%), followed by livestock (635 cases, 21.88%) and wildlife (621 cases, 21.39%). Rabies cases were reported throughout the year, with the majority occurring in the mid-dry season. Hot spots were frequently in the northern and eastern parts of Limpopo and Mpumalanga. Thicket bush and grassland were associated with rabies between 1998 and 2002. However, between 2008 and 2012, cultivated commercial crops and waterbodies were associated with rabies occurrence. In the last period, plantations and woodlands were associated with animal rabies. Of the total number of municipalities, five consistently and repeatedly had the highest rabies prevalence rates. These findings suggest that authorities should prioritize resources for those municipalities for rabies elimination and management. Full article

Background: This study aimed at examining the prevalence and variations in undernutrition among children below five years of age in Uganda while considering the influence of space and time factors. Various studies conducted in Uganda analyzed the undernutrition phenomenon among children below five years of age with a focus on the risk factors and spatial variations; however, no study has ever integrated the elements of time in examining the problem of undernutrition in Uganda. The approach of spatial and spatiotemporal analysis is essential in identifying cluster patterns, hotspots, trends, and emerging hotspots, which is crucial in making timely and location-specific interventions. Methods: Data from the six Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used, with the main outcome variable being undernutrition among children below five years of age. A Composite Index of Anthropometric Failure was derived from the three undernutrition outcomes and subsequently used as a proxy of undernutrition in this study. All data that were relevant to this study were retrieved from the survey datasets and combined with the 2014 shape files of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran’s I, an index of spatial autocorrelation, was used to test the hypothesis of no spatial autocorrelation, while the Getis–Ord (Gi*) statistic was used to examine hot and cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across the country. Moreover, emerging hot spot analysis was done to help in identifying the patterns of undernutrition over time. Results: The national prevalence of undernutrition among children below five years of age was 31.96 percent, with significant spatial variations both in space across Uganda and in the time since 1989. The index of spatial autocorrelation (Moran’s I) confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely, the Karamoja, the Sebei, the West Nile, and the Toro regions, were significantly evident. Most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi, and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive, and intensifying hot spots, West Nile region was identified as a sporadic hotspot area, while the Toro region was identified with both sporadic and emerging hotspots. In conclusions, undernutrition is a silent pandemic that calls for immediate and stringent measures. At 31.96 percent, the prevalence is still very high and unpleasant. To reduce the prevalence of undernutrition and to achieve SDG goal 2, policymakers, as well as implementers, should consider the spatial effects and spatial and spatiotemporal variations across the country and prioritize interventions to hot spot areas. This would ensure efficient, timely, and region-specific interventions. Full article

Traditional hot spot and cluster analysis techniques based on the Euclidean distance may not be adequate for assessing high-risk locations related to crashes. This is because crashes occur on transportation networks where the spatial distance is network-based. Therefore, this research aims to conduct spatial analysis to identify clusters of high- and low-risk crash locations. Using vulnerable road users’ crash data of San Francisco, the first step in the workflow involves using Ripley’s K-and G-functions to detect the presence of clustering patterns and to identify their threshold distance. Next, the threshold distance is incorporated into the Getis-Ord Gi* method to identify local hot and cold spots. The analysis demonstrates that the network-constrained G-function can effectively define the appropriate threshold distances for spatial correlation analysis. This workflow can serve as an analytical template to aid planners in improving their threshold distance selection for hot spot analysis as it employs actual road-network distances to produce more accurate results, which is especially relevant when assessing discrete-data phenomena such as crashes. Full article

This study addresses the knowledge gap regarding the spatiotemporal evolution of Chinese urban agglomerations using long time series of luminescence remote sensing data. The evolution of urban agglomerations is of great significance for the future development and planning of cities. Nighttime light data provide a window for observing urban agglomerations’ characteristics on a large spatial scale, but they are affected by temporal discontinuity. To solve this problem, this study proposes a ridge-sampling regression-based Hadamard matrix correction method and constructs consistent long-term nighttime light sequences for China’s four major urban agglomerations from 1992 to 2018. Using the Getis-Ord Gi* hot-cold spot, standard deviation ellipse method, and Baidu search index, we comprehensively analyze the directional evolution of urban agglomerations and the correlations between cities. The results show that, after correction, the correlation coefficient between nighttime light intensity and gross domestic product increased from 0.30 to 0.43. Furthermore, this study identifies unique features of each urban agglomeration. The Yangtze River Delta urban agglomeration achieved balanced development by shifting from coastal to inland areas. The Guangdong-Hong Kong-Macao urban agglomeration developed earlier and grew more slowly in the north due to topographical barriers. The Beijing-Tianjin-Hebei urban agglomeration in the north has Beijing and Tianjin as its core, and the southeastern region has developed rapidly, showing an obvious imbalance in development. The Chengdu-Chongqing urban agglomeration in the inland area has Chengdu and Chongqing as its dual core, and its development has been significantly slower than that of the other three agglomerations due to the influence of topography, but it has great potential. Overall, this study provides a research framework for urban agglomerations based on four major urban agglomerations to explore their spatiotemporal characteristics and offers insights for government urban planning. Full article

Background: Cancer has become a public health problem and a challenge in developing countries, including Ethiopia. There is scanty local data on cancer epidemiology in Amhara region, Ethiopia. Thus, this study aimed to describe epidemiological characteristics of cancer patients attending Felege Hiwot Referral Hospital. Methods: This study was based on a patient cancer registry that took place in Bahir Dar Felege Hiwot Referral Hospital, Amhara Regional State, Ethiopia. It is the main referral hospital in the Amhara region, and serves more than 5 million people. The hospital has units including oncology for follow-up health care services. All confirmed cancer patients attending oncology units from July 2017 to June 2019 were included in the study. Global Moran’s I statistic was employed to assess spatial heterogeneity of cancer cases across districts. Getis–Ord Gi* statistics was performed to identify hot spot districts with high numbers of cancer cases. Results: In a two-year period, a total of 1888 confirmed cancer patients were registered. There was a significant variation of cancer patients between females (60.8% 95%CI 58.5 to 63.0%) and males (39.3% 95%CI 37.0 to 41.5%). The first three most frequent cancer types seen were breast (19.4%) and cervical cancer (12.9%), and lymphoma (15.7%). Breast and cervical cancer and lymphoma were the first three cancers type among women, whereas lymphoma, sarcoma, and lung cancer were the three most common cancer among men. Spatially, cancer cases were non-random in the study area (global Moran’s I = 0.25, z-score = 5.6, p-value < 0.001). Bahir Dar city administration (z = 3.93, p < 0.001), Mecha (z = 3.49, p < 0.001), Adet (z = 3.25, p < 0.01), Achefer (z = 3.29, p < 0.001), Dangila (z = 3.32, p < 0.001), Fogera (z = 2.19, p < 0.05), and Dera (z = 2.97, p < 0.01) were spatially clustered as hotspot with high numbers of cluster cases. Conclusions: We found that there is a variation in the cancer types with sex. This study provides an insight for further exploration of environmental and occupational exposure related factors for cancer to guide future cancer prevention and control programs. The current study also calls for expansion of cancer registry sites, including in rural areas in the region. Full article

The significance of identifying apple orchard land and monitoring its spatial distribution patterns is increasing for precise yield prediction and agricultural sustainable development. This study strived to identify the optimal time phase to efficiently extract apple orchard land and monitor its spatial characteristics based on the random forest (RF) classification method and multitemporal Sentinel-2 images. Firstly, the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), and Difference Vegetation Index (DVI) between apple orchard land and other green vegetation (other orchards, forest and grassland) during the growing stage were calculated and compared to identify the optimal time phase for apple orchard land extraction; the RF classifier was then constructed using multifeature variables on Google Earth Engine to efficiently identify apple orchard land, and the support vector machine (SVM) classification results were used as a comparison; GIS spatial analysis, a slope calculation model, and Moran’s I and Getis-Ord GI* analysis were employed to further analyze the spatial patterns of the apple orchard land. The results found the following: (1) April, May, and October were the optimal time phases for apple orchard identification. (2) The RF-based method combining coefficients of indexes, the grayscale co-occurrence matrix, and 70% of the ground reference data can precisely classify apple orchards with an overall accuracy of 90% and a Kappa coefficient of 0.88, increasing by 9.2% and 11.4% compared to those using the SVM. (3) The total area of apple orchard land in the study area was 485.8 km², which is 0.6% less than the government’s statistical results. More than half (55.7%) of the apple orchard land was distributed on the gentle slope (Grade II, 6–15°) and the flat slope (Grade I, 0–5°); SiKou, Songshan, and Shewopo contained more than 50% of the total orchard land area. (4) The distribution of apple orchard land has a positive spatial autocorrelation (0.309, p = 0.000). High–High cluster types occurred mainly in Sikou (60%), High–Low clusters in Songshan (40%), Low–High clusters in Sikou (47.5%), and Low–Low clusters in Taocun and Tingkou (37.4%). The distribution patterns of cold and hot spots converged with those of the Local Moran Index computation results. The findings of this study can provide theoretical and methodological references for orchard land identification and spatial analysis. Full article