Urban District Research Articles

Approaching an Urban Night Space Through a 'Virtual' Rhythmanalysis: Public Spaces, Spatio-Temporal Appropriations, Representations, and Online Live Streams

This paper presents an attempt at a 'virtual rhythmanalysis of an urban night space. Rhythmanalysis, as presented by Henri Lefebvre and Catherine Régulier, is a critical practical-theoretical approach to examining the urban space and its modes of temporal production. Across the globe, 24-hour urban lifecycles are increasingly valued in the planning, development, and management of urban districts and spaces. But how does the night affect urban space, and how can we approach it methodically? Utilizing the emergent Lefebvrian rhythmanalysis framework, the paper examines how a public space is (re)produced night after night through spatial and temporal appropriations of the space through the shifting rhythmic constellations of embodied practices and materialities. The paper utilizes screens and online live-camera streams to examine the night space, shifting the rhythmanalysis' original phenomenological-hermeneutic basis towards postphenomenology and mediating visual technologies in the experience of space. The emergence of new audiovisual materials extends our understanding of what the city 'is' and how it can be experientially approached. From a postphenomenological perspective, human-technology relationships are transformed in different ways using audiovisual technologies. Together with addressing the possibilities and challenges of shifting the Lefebvrian `lenses' to a virtual setting, the rhythmanalysis of the urban night space reveals the interplay between strong and weak rhythms and their diurnal oscillation, and the polyrhythmicities of the intersecting flows, bodies, and materialities of the space.

Resistance Capital as a Cultural Asset That Supports Latina Leadership Development & Address Environmental Leadership Challenges in K-12 Schools

ABSTRACT This study delves into the leadership experiences of Latinx women in their professional work contexts who serve as K-12 school principals in public schools located in New Jersey. It accentuates the obstacles faced by these Latina administrators in their roles as school principals to deconstruct negative perceptions and biases toward Latina principals rooted in social identity stereotypes of gender and ethnicity. The study’s findings reveal how their leadership challenges have empowered them to confront microaggressions from intersecting social identity stereotypes related to gender and ethnicity. The primary objective of the research is to dispel misconceptions about Latina school principals and advocate for inclusive leadership practices. The findings indicate that the sustained career success of the Latina school principals in this study was driven by resistant capital (CCW, Yosso & Yosso, 2005; Yosso & Escobedo, 2022), a form of cultural asset that presents an alternative narrative about Latina educational leaders. This study underscores the significance of recruiting and retaining Latina school principals in urban districts and proposes a framework for preparing Latina leaders from K-12 through college.

Dynamic Changes Toward Reflective Practice: Documented Shifts in Speech-Language Pathologists' Evaluation Practices.

This qualitative study explored the experiences of speech-language pathologists (SLPs) in professional learning communities (PLCs) during a year-long virtual professional development (PD) opportunity using an implementation science (IS) framework in an urban public school district for dynamic assessment (DA) training. The DA presented in the PD was modified based on ongoing discussions with key stakeholders in the district, so that it could be reasonably implemented in clinical practice while preserving the integrity of the DA's purpose. SLPs in an urban district were trained virtually using DA over the course of the school year. PD topics focused on decreasing bias and incorporating evidence-based practice into SLP approaches to speech-language evaluations. The SLPs underwent three training sessions and three separate PLC sessions. The SLPs were assigned a series of case studies to complement PLC sessions. An IS framework informed each step of the PD and PLCs to ensure applicability and integration of the content. Of the 366 SLPs working in the district, 209 consented and filled out two questionnaires and three exit slips to gather information on their experiences with the DAs and PLC models. SLPs reported new insights into their practice, including changes in assessment practices, awareness of bias in assessment practices, and increased confidence in working with families during the evaluation process. Two additional themes emerged: increased collaboration and enhanced group problem-solving skills when working with peers in PLC groups, and expanded proficiencies and heightened skills when applying DA practices to evaluations. The two challenges faced by SLPs included time constraints and confidence in the mastery of DA. This study offers an approach to shifting evaluation practices in large districts to incorporate more evidence-based approaches.

On the problem of estimating the snowiness of winters by different methods by the example of the Yuzhno-Sakhalinsk city

The problem of estimating a snowiness (a snow coverage) of a territory is considered by many authors, but still nowadays no common approach to its solution is reached. Different authors use different characteristics in their works for estimating the winter snowiness. In this work the snowiness of winters was estimated using different parameters to determine the most representative one. The area ща study is the Yuzhno-Sakhalinsk urban district. The data for the past 36 years (1986–2022) were used to determine the types of the winter snowiness in this district. The following nine methods have been applied: the maximum winter snow reserve; by the amount of solid precipitation; by the average winter thickness of snow cover from the weather station and by snow survey; by the average greatest ten-day thickness of snow cover; by the amount of precipitation in the form of snow; the method of N.N. Galakhov; the Schultz coefficient; and the maximum winter snow cover thickness. The results obtained are very contradictory. Thus, the types of snowiness completely coincided in only 17% of winters; while in 58% of winters the types of snowiness coincided by 2/3 of the above indicators. Estimation of snowiness using various parameters gives closer results when using data on snow reserve at the beginning of snowmelt and the average winter thickness of snow cover (coincidence in 78% of cases). The reason is that a major part of the above methods uses the values of only one parameter. But given that snowiness is a complex indicator, it would be reasonable to consider all possible parameters at the same time. It is impossible to develop a unified approach to estimating winter snowiness, since the parameters chosen for this problem depend on the goal of the determination. It would also be worthwhile to select a methodology for estimating winter snowiness depending on the tasks set, and on the availability and reliability of the initial meteorological data for the analysis. Such work is also complicated by the insufficient volume of meteorological data, as well as due to gaps in them.

Emerging Trends in E-Commerce in Bangalore Urban District: Opportunities and Challenges for Local Businesses

The rapid growth of e-commerce in India has cemented Bangalore Urban District as a vital area for digital commerce, fueled by its advanced technological infrastructure and consumer base. This study investigates emerging trends such as mobile commerce, omnichannel retail, and social commerce, and examines their impact on local businesses. Through analyzing secondary data and industry insights, this paper identifies factors driving these trends, including high smartphone penetration, an expanding digital economy, and the unique tech ecosystem in Bangalore. The findings underscore the opportunities and challenges for businesses in Bangalore to navigate this competitive sector effectively, providing recommendations on leveraging digital innovations to foster growth.

Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts

Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district locations and their surrounding communities on customer flow. To bridge these gaps, this study utilizes multidimensional big geo-data resources, including mobile phone signaling data, Points of Interest (POI) data, and transportation network data, to uncover the underlying factors that influence customer flow within urban commercial districts. The findings suggest that several factors—the size of the commercial district, the diversity of business formats, the convenience of parking, the working and residential population in surrounding communities, and the proximity to urban centers—significantly influence the customer flow. Consumers show a preference for larger-scale, centrally-located commercial districts that offer convenient parking options, while a homogenized and uncharacteristic business format may reduce a commercial district’s appeal. Furthermore, the study reveals that industrial parks and mixed-use complexes within the 15-minute living circle surrounding the commercial district have a stronger attraction to customer flow than residential neighborhoods do. The insights from this research not only guide the strategic placement of new commercial centers but also provide a robust framework for enhancing the layout of urban commercial spaces and for the revitalization and advancement of established commercial districts.

The Formation and Preservation of Urban Heritage Through Urban Landscape Transformation: A Case Study of Pittsburgh

This study examines the potential of urban landscape transformation to generate and develop new heritage and the role of heritage urbanism in an industrial city. It explores whether the changeover of urban heritage districts in Pittsburgh (PA, USA) can give rise to a novel type of urban heritage. Pittsburgh experienced urban development primarily driven by the presence and accessibility of natural resources, rather than favorable geographical conditions: topography characterized by rugged hills, rock formations, rivers, and stream valleys. The integration of the American-style grid within this unique natural environment resulted in intriguing juxtapositions. Consequently, elements such as bridges, viaducts, stairs, tunnels, and historical inclines gained paramount importance in shaping the urban fabric. The city’s remaining preserved or transformed urban heritage is protected through historic districts designated by the Department of City Planning, which enforces specific planning and design guidelines. The study employs a multi-faceted approach combining the concepts of historic stratification (urban palimpsest), integrated urban morphology, space syntax (integration analysis), and heritage urbanism. During the personally conducted long-term fieldwork, the selected case studies described herein (historic districts, university campus, and traditional neighborhood) proved to be the most suitable for demonstrating urban heritage formation through urban landscape transformation.

A novel automated labelling algorithm for deep learning-based built-up areas extraction using nighttime lighting data

The use of remote sensing imagery and cutting-edge deep learning techniques can produce impressive results when it comes to built-up areas extraction (BUAE). However, reducing the manual labelling set production process while ensuring high accuracy is currently the main research topic. This study pioneers the exploitation of nighttime lighting data (NLD) for automatically generating deep learning label sets, assessing the feasibility, and identifying limitations of using varied intensity ranges of lighting data directly for this purpose. We provide a novel method for generating fine-grained labels through an optimisation technique that eliminates the necessity for human involvement. This approach employs deep learning segmentation algorithms and has been tested in eight cities across seven countries. The results indicate that segmentation performs well in most cities, with the combination of iso clustering and NLD allowing for more precise extraction of urban building districts. The overall accuracy exceeds 90% in most cities. The results based on manual and historical data (around 0.7) as labels are significantly lower than those based on NLD. At the same time, the segmentation effect of deep learning has a more significant advantage over traditional machine learning classification algorithms (around 0.8). DeeplabV3 and U-Net exhibit different strengths in segmentation and extraction: DeeplabV3 has a stronger ability to eliminate errors, while U-Net retains the capability to handle less labelled information, making them mutually advantageous depending on the specific requirements of the task. It proposes a Strategy to automatically extract built-up areas with minimal human involvement.

Assessment of primary school in Changsha based on AISM method

To tackle the challenge of a one-size-fits-all and inconsistent approach to evaluating school development across districts, we have zeroed in on five key urban districts in Changsha City. Fourteen secondary indicators, spanning four critical categories — campus environment, teaching facilities, financial investment, and teacher resources — were selected to comprehensively assess the development of elementary schools in 2021. This assessment was conducted using the Advanced Interpretive Structural Model (AISM). Drawing on the principles of partial order set theory, AISM delves into the partial relationships among these indicators, considering the raw data matrix, optimistic positive and negative distances, and program fit degree. Subsequently, Haas map technology is employed to graphically represent the partial order relationship of elementary school development in each region, providing a clear picture of the hierarchical structure and merit ranking of the evaluation objects. The evaluation results reveal stark differences in the development of elementary schools in Changsha's districts, primary schools in Furong and Kaifu districts stand out for their superior development quality, while Yuhua and Yuelu districts lag behind in terms of elementary school development. Based on these findings, we propose policy recommendations aimed at achieving balanced primary education across districts and ensuring the adequacy of teacher resources.

Teachers as first responders in marginalized, urban communities: Stress and coping during unprecedented times for public education

The present study examines workplace priorities, challenges, and experiences of burnout and coping among teachers in marginalized urban communities, where structural barriers elevate job demands and minimize job resources. Specifically, we amplify the voices of public elementary school teachers in an urban hub of Florida during a time of unprecedented stressors (COVID-19, staff shortages, racial injustice, school shootings) to identify possible avenues of support. Participants (N = 12) were teachers in grades 3 through 5 from one large urban district; six participants served predominately Black or African American students, and six participants served predominately Hispanic students. All teachers self-identified as female and from a racially and/or ethnically minoritized background themselves. Semi-structured interviews (conducted remotely by the first author) were organized around five questions. Qualitative content analysis highlighted two core priorities (support students holistically and address interconnecting social emotional skills and academic success) and two core challenges (mismatch between administration expectations/supports and teacher needs/values, and difficulty managing student behavior). Teachers attributed the ubiquitous experience of burnout to factors at levels of work (e.g., responding to student stressors and traumas on top of curriculum demands), organization (e.g., poor compensation, excessive work demands), and society (e.g., school shootings, COVID-19), and they offered concrete recommendations for improving their work conditions. Altogether findings inspire opportunities to reevaluate supports for teachers serving as first responders for youth in marginalized urban communities.

Eye disease knowledge and awareness among Iranian population: The Gilan Eye Study

The purpose of this study was to investigate the public knowledge regarding cataract, glaucoma, refractive error, and diabetic retinopathy among Iranian population living in Gilan province. This population-based cross-sectional study was conducted on 2588 individuals aged over 50, living in both urban and rural districts of Gilan. All participants were interviewed to determine their knowledge regarding the mentioned eye diseases. Their source of knowledge was determined whether health care professionals, family or relatives, social media, or publications. Socioeconomic status (SES) of participants was calculated based on the principal component analysis. The presenting visual acuity of all study participants was assessed by a trained optometrist. In this study, total knowledge of cataract, glaucoma, refractive errors and diabetic retinopathy was 52.1%, 2.5%, 15.1%, and 29.7%, respectively. A lower cataract knowledge was found in participants with low (odds ratio [OR] = 0.44; p = .02) and moderate SES (OR = 0.56, p = .02). We also lower knowledge of glaucoma among low (OR = 0.33; p < .001) and moderate SES group (OR = 0.48, p = .002). A higher level of knowledge about refractive error was observed in participants with normal vision (OR = 4.89; p = .011) and mild/moderate visual impairment (OR = 3.69; p = .046). Participants without a history of diabetes mellitus had less knowledge about diabetic retinopathy (DR) (OR = 0.56; p < .001), while those with more than 12 years of education had higher knowledge about DR. In conclusion, lower level of the knowledge was identified among individuals with lower levels of SES and education, while participants with better vision could be benefit from the high levels of knowledge.

Spatially Varying Regression Coefficient Model For Predicting Stunting Hotspots In Indonesia

Stunting is a significant issue, particularly in the context of Indonesia. Identifying crucial risk factors is crucial for mitigating and developing effective strategies to control stunting. A Bayesian approach was employed to develop a regression model that incorporates spatial variation, allowing risk factors to vary across different districts and cities. The aim was to obtain the most optimal regression model. The analysis revealed that the impact of immunization varies across districts and cities in Indonesia when it comes to explaining the differences in stunting prevalence. The hotspot prediction results indicate that most urban districts in Indonesia remain hotspot areas, with a stunting risk exceeding 20%. The government must ensure the effective implementation of the immunization program in order to mitigate the prevalence of stunting in Indonesia. The novelty of this research lies in the use of Bayesian approaches to spatial analysis in identifying and understanding stunting risk factors as well as the prediction of stunting hotspots in Indonesia. This approach provides in-depth insight into local variations in the prevalence of stunting and the effectiveness of health interventions, which supports more effective and targeted policy development.

Design discharge estimation for small urban districts – an analytical approach

ABSTRACT This paper presents a straightforward design discharge estimation procedure for small urban districts. In this approach, the configuration of the small urban district was simplified into either a rectangular porous overland plane or a V-shaped porous overland model. The Horton equation was used to estimate the infiltration on the overland planes, and the kinematic-wave approximation was applied for runoff simulations. In deriving the runoff equation, the Darcy–Weisbach coefficient was adopted to account for the influences of spatially various flow depths on overland flow resistance. The theoretical derivations were applied to a series of cases and verified by numerical models to demonstrate the applicability of the proposed method. The results showed that the design discharges obtained from the analytical equations are close to those simulated by the numerical model. The analysis procedure developed herein can provide a convenient way for engineers to conduct urban sewer designs.

Impact of gender-biased parental perceptions on under-immunization in Eastern Sudan: a cross-sectional study.

Despite global efforts, inequities in vaccine uptake remain, influenced by socioeconomic, geographic, cultural, and gender-related factors. In Eastern Sudan, gender disparities are acknowledged, particularly in livelihoods, but their impact on vaccination uptake is unclear. This study aimed to assess the effect of gender-biased parental perceptions on under-immunization among children in Kassala, Eastern Sudan. This study was a community-based cross-sectional in rural and urban districts of Kassala locality in Kassala State, Eastern Sudan in November 2022. Data were collected from parents using a pre-tested, structured questionnaire. The Chi-square or Fisher's exact test was conducted to assess the factors associated with under-immunization among children. Data were collected from 400 parents. Our data reveal that most children were fully vaccinated with the three doses of the pentavalent vaccine (83%), while 14% were partially vaccinated. Findings showed that about one in five parents perceived male vaccination as more important than female vaccination. This parental perception of gender-based importance in vaccination was significantly associated with under-immunization among children (p-value = 0.049). Additionally, males in our study are fully vaccinated 5% more often than females. Socio-economic factors, including mothers' education and households' income level, were also significantly associated with the vaccination status of the children. This study shed light on the effect of gender norms and related determinants on equitable access to vaccinations for boys and girls alike. More research is needed to gain a better understanding of the gender norms related to vaccination and their long-term impact on immunization demand and resilience in this region.

The gender difference in cause of death in Japanese atrial fibrillation patients: The Fushimi AF Registry

Abstract Background Previously we reported that incident rate of heart failure (HF) hospitalization was higher in female than male and stroke was comparable between female and male in Japanese atrial fibrillation (AF) patients. We also reported cardiovascular death was mainly related to HF but not stroke. However, the gender difference in cause of death in Japanese AF patients is little known. Method The Fushimi AF Registry is a community-based prospective survey of AF patients in Fushimi-ku, which is a typical urban district of Japan. We started to enroll patients from March 2011, and follow-up data were available for 4,496 patients by the end of February 2022. In the entire cohort, 2,677 were male and 1,819 were female. We compared clinical characteristics and cause and incidence rate of death between two groups. Result Female patients were older (female vs. male; 76.7 years vs. 71.5 years, p<0.01), less in body weight (51.2 kg vs. 65.0 kg, p<0.01), had higher pulse rate (79.4 bpm vs. 77.5 bpm, p<0.01), less often persistent/permanent type (48.2% vs. 52.0%, p=0.04), less likely to have previous stroke/systemic embolism (18.6% vs. 21.1%, p=0.04), diabetes mellitus (19.9% vs. 26.2%, p<0.01), coronary artery disease (12.3% vs. 16.0%, p<0.01) and more likely to have prior HF (32.2% vs. 24.0%, p<0.01), chronic kidney disease (39.3% vs. 33.7%, p<0.01)and had higher CHADS2 score (2.15 vs. 1.96, p<0.01). In prescription data, female patients less often received oral anticoagulant (52.1% vs. 58.2%, p<0.01) and antiplatelet drug (23.8% vs. 29.0%, p<0.01). Systolic blood pressure, hypertension and major bleeding were comparable between two groups. During the median follow-up of 2,093 days, the incidence rate of all cause death was comparable between female group and male group (5.20 vs. 4.86 % per person-year, log rank p=0.32). The incidence rate of cardiac death was significantly higher in female group than male group (0.98 vs. 0.70 % per person-year, log rank p=0.02) and the incidence rate of vascular death was also significantly higher in female group than male group (0.43 vs. 0.26 % per person-year, log rank p=0.03). The incidence rate of death due to HF, which was main cause of cardiac death, was higher in female group but not significant (0.86 vs. 0.66 % per person-year, log rank p=0.08). We analyzed the proportion of cause of death in each gender, cardiac death and stroke were higher and non-cardiovascular death was lower in female than male group. Conclusion The incidence rates of cardiac death and vascular death were significantly higher in female group than male group, and death due to HF was non-significantly higher in female group. Concerning the proportion of cause of death in each gender, cardiac death and stroke were higher in female than male. The impact of cardiovascular events may be stronger and prevention of them may be more important in female.

Survey on Incidence of Gummy Stem Blight on Watermelon (Citrullus lanatus) in Karnataka, India

Watermelon (Citrullus lanatus) is an important cucurbitaceous crop cultivated worldwide. The cultivation of watermelon and other species of the family Cucurbitaceae is severely affected by several diseases like anthracnose, Alternaria leaf spot, bacterial fruit blotch, powdery mildew and gummy stem blight. Among the diseases, gummy stem blight is an emerging and devastating disease caused by Didymella bryoniae producing a serious threat to the economics of production of cucurbits like watermelon. A roving survey was carried out in eight districts of Karnataka (Chamarajanagara, Chikkaballapura, Gulburga, Vijayapur, Bengaluru urban, Kolar, Mysuru and Yadgiri) from 2021 to 2023 to record the incidence of gummy stem blight (GSB) disease on watermelon. The survey data revealed that GSB disease was observed in all areas. The per cent disease index ranged from 29.6 to 51.2 per cent. The maximum mean per cent disease index of (51.2%),) Bengaluru urban district and the lowest was seen in the Mysuru district (29.6%) followed by Chikkaballapura district (31.0%). The pathogen associated with gummy stem blight disease was isolated and proved the pathogenicity of the isolate and fulfilled the Koch’s postulates.

Tax credit for support of university-community partnerships in low-income urban school districts

Tax credit for support of university-community partnerships in low-income urban school districts

Quantitative Analysis of the Future of the Sharing Economy, Housing, White-Collar Labor Markets, and Problems in Big Cities

This research project explores the future of the sharing economy and its profound effects on housing markets, white-collar labor dynamics, and socio-economic challenges in large metropolitan areas. Using quantitative economics and econometric techniques, the study evaluates how platforms such as Airbnb, Uber, and coworking spaces are reshaping urban life, exacerbating housing affordability issues, and altering labor market structures. By analyzing housing market data, labor statistics, and platform economy data from major cities like New York, London, and Istanbul, this research provides empirical insights into how the sharing economy influences urban economic landscapes. The study employs difference-in-differences (DiD) and multivariate regression models to assess causal relationships, particularly in terms of housing market disruptions caused by short-term rental platforms and labor market fragmentation driven by the rise of gig and remote work. Spatial econometric techniques are also used to explore geographic disparities in housing and employment impacts across urban districts. Preliminary findings indicate that the proliferation of sharing economy platforms has contributed to housing shortages and increased prices in central urban areas, while labor markets experience growing polarization, with high-skilled workers benefiting from remote work opportunities and low-skilled gig workers facing economic precarity. This research offers valuable policy insights into the regulation of short-term rentals and the protection of gig economy workers, highlighting the need for regulatory frameworks that address urban inequality and labor market shifts. By integrating data from multiple sources, this study contributes to the academic discourse on urban economics and provides a framework for understanding the long-term socio-economic implications of the sharing economy in big cities.

Assessing the adoption level of solar PV installations, on district scale of urban environment

This study addresses a research gap by implementing a novel methodology that combines GIS and MCDM techniques to evaluate photovoltaic (PV) potential at an urban scale, specifically within Riga, Latvia. Utilizing the AHP and MABAC methods, the research integrates technical, social, and economic criteria to rank urban districts based on their suitability for PV installations. 58 different districts were evaluated according to technical and socioeconomic criteria and were ranked through the MABAC MCDM methodology, from most preferable to less, on a PV adoption level. This comprehensive approach not only identifies the most promising areas for rooftop PV deployment but also emphasizes the importance of considering socioeconomic factors to enhance equity in renewable energy adoption. The findings provide valuable insights for urban planners and policymakers, demonstrating a systematic, data-driven method to promote sustainable energy initiatives in urban environments.

Monitoring of Heracleum sosnowskyi Manden Using UAV Multisensors: Case Study in Moscow Region, Russia

Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student’s t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication.