National Mapping Research Articles

Modeling of flood regulation for ecosystem accounting: a case study of Ogosta river basin

The System of Environmental-Economic Accounting – Ecosystem Accounting (SEEA-EA) is a spatially-based, integrated statistical framework for organizing biophysical information about ecosystems, measuring ecosystem services (ES). Water flow regulation ES and biophysical modeling are among the main topics in the individual ES part of the SEEA-EA framework and flood regulation ES is one of the important services. Characterizing and assessing flood regulation is a challenging task as both assessment and accounts of this ES need various data which are usually not available through direct or indirect measurements, therefore modeling approaches of water regulation are much needed. Despite growing attention and studies using hydrologic models to assess and/or map flood regulation ES, the accounting of this service is still not well developed. In this paper, we present an approach for accounting flood regulation at a local scale using ArcSWAT modeling. It is based on the results of flood regulation ES assessment, where modeling results are used to quantify the ES indicators and delineate the service providing areas (SPA) and service demand areas (SDA). The actual flow of flood regulation is calculated as a ratio between ES demand and ES potential and it represents the area of SPA which corresponds to the demand for flood regulation represented by SDA. The results show that predominant flood regulations ES supply is provided by the forest ecosystem as well as the actual flow. The accounting of flood regulation is strongly determined by ecosystem extent mapping. The CORINE Land Cover (CLC) provides the most appropriate and available data for mapping ecosystem extent at smaller scales. However, at a larger scale, it is too coarse and the combination of Mapping and Assessment of Ecosystems and their Services (MAES) national ecosystem mapping gives better results.

MAINTENANCE AND UPDATE NATIONAL TOPOGRAPHIC DATA BASE IN HIGH FREQENCY USING MODERN PROCEDURES AND TECHNIQUES

Abstract. The state of Israel is a small country, in terms of area, with one the higher population growth rate among all the OECD countries. Hence, each year more than 50,000 housing units built and more than 1,500 km roads constructed in average, each year along the last decade.The Survey of Israel )SOI( is a National Mapping and Cadastral agency (NMCA) that holds the responsibility to update, maintain and disseminate the National Topographic Data Base (NTDB) to all of the governmental offices and municipalities, to utilize, plan and promote the construction of the public needs.Today, the reality is dynamic and the changes occurs every day, therefore, the users expect that the existing database will portray the current reality as possible. In order to address the user's requirements for rapid updating rate, the Survey of Israel promote a new and modern initiatives, including:Promote the cooperation and working relationship with the municipalities to rapidly update the streets, address and POI’s.Data Fusion from different governmental and municipalities data sources.Developing change detection techniques to update the buildings and roads layers.Developing new frameworks for the manual inspection and QA/QC procedures.Developing automatic tools to update the height component via high resolution DSM and DTM (50cm GSD).Disseminate quarterly the NTDB products to the users.Develop a modern processes management system for the update and QA workflowIn this article, we will demonstrate the initiatives and techniques that improved our NTDB updating rate to less than a year and a half in a national average.

CNN-BASED TEMPLATE MATCHING FOR DETECTING FEATURES FROM HISTORICAL MAPS

Abstract. Efficiently detecting features from historical maps is a challenging task due to its inconsistent manual scribbling styles and the lack of large scale labelled training data. To tackle this issue, this paper proposes an automatic feature detection pipeline utilizing CNN-based template matching (TM), which can lead to efficient feature extraction with minimal input, i.e. one single template. Three CNN-based TM models equipped with different feature extractors are investigated and compared in this research, namely pre-trained VGG19 CNNs, autoencoders, and the combination of both. Experiments conducted on six tiles of the Swiss Old National Map demonstrate that the combined architecture achieves the best result in wetlands detection, resulting in a mean intersection over union (IoU) of 69% and an average F1 measure of 82%.

Approach and solutions to congenital hearing impairment in Cameroon: perspective of hearing professionals

ObjectivesTo bring out the diagnostic attitude of hearing professionals in Cameroon towards congenital hearing impairment (CHI), assess availability of tests, neonatal screening, and create a national map of availability of treatment opportunities.MethodsWe conducted a cross-sectional online-based survey from June to December 2021, concerning ear–nose–throat (ENT) specialists, hearing care professionals, speech therapists and ENT nurses. A Google Forms online questionnaire was used to collect data, filled by eligible professionals involved in hearing care in Cameroon.ResultsA total of 93 professionals working in 31 different health facilities participated. A cumulative percentage of 79.9% of ENTs were found in just two out of 10 regions. Specialists sought by ENTs for assessment of patients with CHI included neurologists/neuro-pediatricians (96.8%), pediatricians (47.6%), other ENTs (34.9%), and psychologists (3.2%). Investigations requested included auditory-evoked brainstem response (ABR; 87.3%), otoacoustic emissions recording (OAE; 71.4%), and tympanometry (66.7%). There were eight OAE and nine ABR machines in the country. Twenty-five (88.6%) out of 31 facilities with otolaryngologists did not carry out systematic neonatal screening. Reasons included unavailability of equipment (21; 84%), and administrative delays (14; 56%). Sixteen (51.6%) facilities had ENTs with additional training in otologic surgery and 11 (35.5%) were equipped to perform ear surgery. Three centers (9.7%) specialized in hearing aid provision and maintenance services. Three hospitals (9.7%) had performed cochlear implantation.ConclusionOur results show scarcity and overt unevenness in distribution of specialists, equipment and solutions to CHI in Cameroon. A serious negative health care consequence of this shortage is the unavailability of universal newborn hearing screening and implementation programs.

BUILDING CHANGE DETECTION IN AIRBORNE LASER SCANNING AND DENSE IMAGE MATCHING POINT CLOUDS USING A RESIDUAL NEURAL NETWORK

Abstract. National Mapping Agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) sensors as well as using Dense Image Matching (DIM) on aerial images. As these datasets are often captured years apart, they contain implicit information about changes in the real world. While detecting changes within point clouds is not a new topic per se, detecting changes in point clouds from different sensors, which consequently have different point densities, point distributions and characteristics, is still an on-going problem. As such, we approach this task using a residual neural network, which detects building changes using height and class information on a raster level. In the experiments, we show that this approach is capable of detecting building changes automatically and reliably independent of the given point clouds and for various building sizes achieving mean F1-Scores of 80.5% and 79.8% for ALS-ALS and ALS-DIM point clouds on an object-level and F1-Scores of 91.1% and 86.3% on a raster-level, respectively.

A robust but straightforward phenology-based ginger mapping algorithm by using unique phenology features, and time-series Sentinel-2 images

Ginger (Zingiber officinale), a commonly used spice, dietary supplement, and medical plant, is expanding rapidly in China due to its increasing demands and price in the international market. However, the existing information on its area and spatial distribution can only be derived from statistical data, and spatially explicit maps of ginger planting areas are unavailable. We proposed a simple but robust ginger planting area mapping algorithm using the time-series Sentinel-2 images and Google Earth Engine (GEE) platform. Changyi city, the hotspot city with the highest ginger production in China, was selected as our study area. Two ginger indices (Ginger phenological features index and Ginger cultivating management index) were generated based on the four unique phenology features: 1) high reflectance at the start of the growing season due to white agricultural mulching films, 2) a slower green-up feature in the early season, 3) low reflectance in summer due to black sunshade nets cover, 4) a unique green feature at the end of the growing season. The accuracy assessment based on the ground truth data showed fairly reasonable overall accuracies of 96.7%–97.3%, while the Matthews correlation coefficients (MCC) were 0.871–0.924. We found a rapid ginger expansion from 63.0 km2 in 2019 to 101.5 km2 in 2021 in the study area, by 61% from 2019 to 2021. Moreover, about 78% of ginger land in the study area had crop rotation every two years to boost yield and prevent bacterial diseases in the soil. Our study identified four critical features of ginger and demonstrated the potential of the phenology-based approach, Sentinel-2 data, and GEE platform for ginger mapping. This study is expected to apply to regional or national ginger mapping to provide valuable information for the ginger planting plan and guide the international ginger trade.

Development of probabilistic seismic hazard microzonation maps at the surface level for central-east Iran (Kerman region), using a hybrid site condition model

This study presents the spatial distribution of a grid-based probabilistic seismic hazard model at the surface level for the Kerman region in central-east Iran, using a hybrid site condition model. The hazard model has provided the variation of the peak ground acceleration (PGA), and 5% damped spectral accelerations (SA) at the fundamental periods of 0.2 and 1.0 seconds (s) on 0.05° grids. The study has used a seismotectonic dataset including ∼400 linear seismic sources to characterize the seismic source models. In order to capture the uncertainties of the seismic hazard sources, the model used a logic tree approach, including eight ground motion prediction equations (GMPEs), three characteristic source models, two different declustering methods, three choices for earthquake recurrence parameters, and three alternatives for the maximum credible earthquake. Besides, various GMPEs have been evaluated against local datasets. For this, a series of GMPEs developed based on local, regional, and global data have been selected to find their compatibility with the studied area through statistical analyses, so-called likelihood (LH), and log-likelihood (LLH) tests. Generally, the results display that a wide range of seismic hazards can be expected in the region. For instance, the horizontal PGA with a 10% probability of exceedance in 50 years, ranges between ∼(0.1–0.5)g in the north-eastern and south-western parts of the region, respectively. Moreover, the results indicate that the ad-hoc 1.5 factor to scale a 475-year return period (RP) spectrum for making the ground shaking similar to a 2475-year RP not only is unrealistic, but also creates substantially an underestimated ground shaking; thus, it seems too crude for the seismic design purposes. We have also developed some relationships, dependent on site conditions, for describing general relations between the obtained results in the return periods of 475- and 2475-years for PGA, SA (0.2s), and SA (1.0s). The computed hazard spectra have also been compared to the minimum acceptable level of seismic hazard in the region, according to the latest version of the Iranian national seismic hazard map. In addition, in order to make the results more transparent to the scientific and engineering community, the range of uncertainty contributed to the hazard modeling has also been explored in terms of a relative uncertainty metric. In total, the outcome of this study can make transparency into the picture of the seismic hazard level in central-east Iran and shed insight into possible developing plans.

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Improvements to Drinking Water

Taumata Arowai, the new independent water services regulator, recently consulted publicly on the drinking water rules for water suppliers. We use a case study on nitrate and official information requests to demonstrate the current weaknesses in the drinking water monitoring and reporting systems and why the reforms proposed by Taumata Arowai seem unlikely to substantively address many of these deficiencies. To ensure sufficient public health surveillance and robust epidemiological research into the potential health impacts of drinking water contaminants, Taumata Arowai should: 1) establish a national database for water supply and quality; 2) mandate the standardisation of reporting requirements across water suppliers; 3) increase the frequency and range of water quality testing; and 4) maintain a national map of water supplies. These upgrades are particularly important in an era of rapid land use changes and climate change.

Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

PERAN SOE HOK GIE DIDALAM PERJUANGAN MAHASISWA ANGKATAN 66 MELAWAN KEKUASAAN PEMERINTAHAN ORDE BARU

Soe hok gie as a literature student at the university of Indonesia played an active role in the struggle of the students of the 66th generation to overthrow the power of the old order led by president Soekarno. His harsh criticism reached several corners so that he was famous among students. An idealistic attitude and the use of harsh language are used to convey his aspirations. The students of the 1966 generation were indeed very strong and well-known for their actions at that time to voice TRITURA after the g 30s rebellion. The precarious situation at that time made students from various universities unite and hold demonstrations. This makes students considered in the national political map.

On Finding a Projected Coordinate Reference System

The digital age has brought about an explosion in the growth of data, of which data with a geographical component stands out. Proper use of geographical data comes with the need for correct coordinate reference systems (CRSs). They are considered the ultimate binder for interoperability between geospatial data actors and stakeholders. Moreover, CRSs are crucial for the visual and analytical integration of geospatial data from disparate data sources. However, CRSs might be—for numerous reasons—incorrectly assigned or even missing. The result is a time-consuming study of the map, literature, and available resources to ultimately find the alleged right CRS. This study provides a summary of prevailing resources from national mapping agencies of some European countries to address the above problem. Secondly, and most importantly, is the development of an open-source Python-based software package. This software package aims to accurately estimate the best candidate CRS, given a tuple of coordinates at a priori an approximately known location. It is controlled by geocoding the known location and intersecting the resulting coordinate with the bounding box of all CRSs in the EPSG-database. An in-depth review of CRS tools by mapping authorities reveals, in particular, limitations concerning the countries’ spatial areas, in combination with often required know-how of local CRSs. To address these shortcomings, our tool is developed to enable a more generic extraction of CRSs for any given location worldwide. Testing proved successful for 30 different maps, with a grid present on the map and the CRS of the map being included in the EPSG-database.

Revising Cadastral Data on Land Boundaries Using Deep Learning in Image-Based Mapping

One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries.

Using machine learning to produce a cost-effective national building height map of Ireland to categorise local climate zones

Abstract. ECOCLIMAP-Second Generation (ECO-SG) is the land-cover map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM Numerical Weather Prediction system used for short-range operational weather forecasting for Ireland. The ECO-SG urban classification implicitly includes building heights. The work presented in this paper involved the production of the first open-access building height map for the island of Ireland which complements the Ulmas-Walsh land cover map, a map which has improved the horizontal extent of urban areas over Ireland. The resulting building height map will potentially enable upgrades to ECO-SG urban information for future implementation in HARMONIE-AROME. This study not only produced the first open-access building height map of Ireland at 10 m × 10 m resolution, but assessed various types of regression models trained using pre-existing building height information for Dublin City and selected 64 important spatio-temporal features, engineered from both the Sentinel-1A/B and Sentinel-2A/B satellites. The performance metrics revealed that a Convolutional Neural Network is superior in all aspects except the computational time required to create the map. Despite the superior accuracy of the Convolutional Neural Network, the final building height map created results from the ridge regression model which provided the best blend of realistic output and low computational complexity. The method relies solely on freely available satellite imagery, is cost-effective, can be updated regularly, and can be applied to other regions depending on the availability of representative regional building height sample data.

Imperial Cartography and National Mapping in Afghanistan

Afghanistan assumed its modern cartographic form in piecemeal fashion between the late 1860s and early 1890s in the context of British imperial boundary-making projects in South Asia and the Middle East. The bordering of Afghanistan was contextualized by the global British empire and multiple boundary conflicts and frontier anxieties involving the British and the French, German, Russian, and other imperial powers as well as local rulers. The fundamental point here is that the map of Afghanistan is a product of imperial and interimperial concerns, and it has not benefited the Afghan people. The map of Afghanistan may or may not have served the imperial purposes for which it was created, but more importantly, by uncritically accepting it and its imperial heritage, we Afghans have become victims of an imperial map of ourselves. Afghans and non-Afghans will benefit from directly confronting the coercive impact of imperial mapping agendas on the largely invisible people “on the map” of Afghanistan. This essay historicizes the production of maps of modern Afghanistan, exposing imperial and crypto-colonial influences upon our national cartography. In so doing, it critically reimagines our spatial politics and reconfigures our intellectual infrastructure. It is an exercise in historical recentering designed to instill Afghan humanity and agency onto the map of Afghanistan.

Territorializing spatial data: Controlling land through One Map projects in Indonesia and Myanmar

Once confined to paper, national cartographic projects increasingly play out through spatial data infrastructures such as software programs and smartphones. Across the Global South, foreign donor-funded digital platforms emphasize transparency, accountability and data sharing while echoing colonial projects that consolidated state-based territorial knowledge. This article brings political geography scholarship on state and counter-mapping together with new work on the political ecology of data to highlight a contemporary dimension of territorialization, one in which state actors seek to consolidate and authorize national geospatial information onto digital platforms. We call attention to the role of data infrastructures in contemporary resource control, arguing that territorializing data both extends state territorialization onto digital platforms and, paradoxically, provides new avenues for non-state actors to claim land. Drawing on interviews, document review, and long-term fieldwork, we compare the origins, institutionalization and realization of Indonesia and Myanmar's ‘One Map’ projects. Both projects aimed to create a government-managed online spatial data platform, building on national mapping and management traditions while responding to new international incentives, such as climate change mitigation in Indonesia and good democratic governance in Myanmar. While both projects encountered technical difficulties and evolved during implementation, different national histories and political trajectories resulted in the embrace and expansion of the program in Indonesia but reluctant participation and eventual crisis in Myanmar. Together, these cases show how spatial data infrastructures can both extend state control over space and offer opportunities for contesting or reimagining land and nation, even as such infrastructures remain embedded in local power relations.

Finite Mixture Models in the Evaluation of Positional Accuracy of Geospatial Data

Digital elevation models (DEMs) are highly relevant geospatial products, and their positional accuracy has demonstrated influence on elevation derivatives (e.g., slope, aspect, curvature, etc.) and GIS results (e.g., drainage network and watershed delineation, etc.). The accuracy assessment of the DEMs is usually based on analyzing the altimetric component by means of positional accuracy assessment methods that are based on the use of a normal distribution for error modeling but, unfortunately, the observed distribution of the altimetric errors is not always normal. This paper proposes the application of a finite mixture model (FMM) to model altimetric errors. The way to adjust the FMM is provided. Moreover, the behavior under sampling is analyzed when applying different positional accuracy assessment standards such as National Map Accuracy Standards (NMAS), Engineering Map Accuracy Standard (EMAS) and National Standard for Spatial Data Accuracy (NSSDA) under the consideration of the FMM and the traditional approach-based one-single normal distribution model (1NDM). For the NMAS, the FMM performs statistically much better than the 1NDM when considering all the tolerance values and sample sizes. For the EMAS, the type I error level is around 3.5 times higher in the case of the 1NDM than in the case of the FMM. In the case of the NSSDA, as it has been applied in this research (simple comparison of values, not hypothesis testing), there is no great difference in behavior. The conclusions are clear; the FMM offers results that are always more consistent with the real distribution of errors, and with the supposed statistical behavior of the positional accuracy assessment standard when based on hypothesis testing.

AIMing for Equity in Aviation Accessibility: Development of the Aviation-accessibility Integrated Mobility (AIM) Metric

As federal spending and planning for air transportation infrastructure looks to prioritize access for disadvantaged populations, aviation systems planning metrics that measure accessibility at the individual level are necessary. Existing metrics, from the mobility-driven metrics focused on efficiency and on-time performance to geographic accessibility metrics focused on connectivity, lack the detail of the multiple, interlocking constraints that limit potential travelers (especially lower-income travelers) from executing their agency and accessing the aviation system. We seek to develop a methodology, resulting in new analysis metrics, to quantify accessibility on an origin–destination basis based on individual constraints, time- and cost-based impedance, and aviation travel supply. We develop and apply our Aviation-accessibility Integrated Mobility (AIM) metric to empirically model relative accessibility based on traveler-specific constraints, accounting for individual-level sensitivity to travel costs and propensity to travel by ground access modes. We illustrate how equity-focused variables can change the calculus and geographic distribution of accessibility by applying the AIM to our case study region: Philadelphia, Baltimore, and Newark metropolitan areas, a region with significant socioeconomic disparities, to diverse markets (Austin, Atlanta, Nashville). Our findings indicate that incorporating individual constraints greatly influences the calculation of accessibility; additionally, we find that transportation supply and service characteristics alter the distribution of accessibility. Our model supports a national map of accessibility and potential policy recommendations to expand traditional federal airport infrastructure projects, such as targeted air service enhancement.

Socioeconomic inequality in Scottish children’s exposure to and use of natural space and private gardens, measured by GPS

The objectives of this study were to: (i) determine, by distance from home, the proportion of time children spent in natural space (NS), private gardens (PG) and natural space/private garden (NS/PG); (ii) calculate availability of these environments surrounding homes, and (iii) explore variation in availability and use by socio-economic status (SES).Detailed mobility data for 10/11-year-old children (n = 667) were obtained. Children wore GPS devices and locations were spatially joined to UK national mapping data (Ordnance Survey) to identify if each one was within NS, PG or NS/PG. Euclidean distance between GPS points and home was measured and discretised into 100 m bands (100 m to 800 m).Children spent 15% of their total outdoor, non-school wear time in NS, but 41% in NS/PG. Both time spent in NS & NS/PG and its distance from home varied by SES. Children living in the most deprived areas spent 17% of their total wear time in NS/PG within 100 m from home, and 4.4% in NS/PG over 800 m from home. In contrast, children from the least deprived areas spent 19% of wear time in NS/PG less than 100 m from home and 10.7% in NS/PG over 800 m from home. An increase in the availability of NS and NS/PG around the home was weakly associated with increased use.NS and PG provide a key location that children spend their outdoor time, particularly in areas close to home for those from more deprived areas. Children from the least deprived areas have greater exposure to NS, most of which occurs away from home.

Relation of migration trend and gender of RMG labour force in Industrial areas in Bangladesh

Legitimate labour migration has, no doubt, got much attention in various economies and labour force irrespective of gender tempt for diverse employment. Industrial employment and job concentration in industrial cluster always provoke work driven migration. People relocate their whereabouts based on their secure livelihoods. And, economic centralization and targeted migration thus associate each other with no exception in Bangladesh. In the 5-decade long industrial history, many changes, reforms and challenges were noticed throughout our industrial ecosystem and labour dynamics. Labour force is a critical strength for efficient and low-cost productivity. In this regard, our mixed labour force, skilled, semi and unskilled workforce, has shaped our local industry and positioned as the most competitive regionally and contributed to gain productivity momentum and foreign trade growth. RMG industry, the forefront export-oriented sector of economy, has emerged as the largest employer of local migrated workforce of all contemporary industries in Bangladesh encouraging labour migration in urban area. This industry shares relatively large in GDP and export earning. With unprecedented contribution by RMG to economy, this study attempted to know whether any relationship of migration trend and gender status since this industry is a female dominant industry. The assessment of correlation is justifiably demanded due to women employment intensity since inception. But, this quantitative research-cause and effect study resulted that gender and migration condition and flow have no positive relationship and integration upon applying non-parametric test Chi-square test of significance. Around 252 independent samples from RMG factories in Dhaka and nearby districts where industrial hubs are located. It has obtained the null and rejected alternative hypothesis. These findings may help for national perspective planning and development planning. The drawn outcomes may also enable us national resource planning, mapping and human development index. As well, we can measure labour productivity in RMG industry and plan for future facility as well urban living and economic development.