The study explores the phenomenon of street harassment and its socio-psychological implications on women living in the capital city of Islamabad, Pakistan. Street harassment represents a form of gender violence that violates human dignity by making the female victims feel vulnerable and insecure in public spaces. The findings have been compiled on the basis of empirical analyses of the views of 200 female students from eight different universities of Islamabad, Pakistan. The study reveals that objectification, street harassment and abuse are incontrovertible parts of women’s lives in Pakistan. Despite constituting more than half of the population in the Pakistan, women continue to live in a patriarchal milieu that thrives on harassment, discrimination and oppression against them in both public and private spheres. The study concludes that the menace of street harassment entails devastating emotional, physical and psychological impacts and recommends that policy interventions be undertaken to curb this heinous act perpetrated against women.

Uncontrolled urban expansion resulting from urbanization has a disastrous impact on agricultural land. This situation is being experienced by the densely populated and fertile island Java in Indonesia. Remote sensing technologies have developed rapidly in recent years, including the creation of Google Earth Engine (GEE). Intensity analysis (IA) is increasingly being used to systematically and substantially analyze land-use/land-cover (LULC) change. As yet, however, no study of land conversion from agriculture to urban areas in Indonesia has adopted GEE and IA approaches simultaneously. Therefore, this study aims to monitor urban penetration to agricultural land in the north coastal region of West Java Province by applying both methods to two time intervals: 2003–2013 and 2013–2020. Landsat data and a robust random forest (RF) classifier available in GEE were chosen for producing LULC maps. Monitoring LULC change using GEE and IA has demonstrated reliable findings. The overall accuracy of Landsat image classification results for 2003, 2013, and 2020 were 88%, 87%, and 88%, respectively. IA outputs at interval levels for all categories showed that the annual change-of-area rate was higher during 2013–2020 than during 2003–2013. At the category level, IA results showed that the area of agricultural land experienced net losses in both periods, with net loss in 2013–2020 being 2.3 times greater than that in 2003–2013 (∼1,850 ha per year). In contrast, the built-up area made net gains in both periods, reaching almost twice as much in the second period as in the first (∼2,030 ha per year). The transition-level IA performed proved that agricultural land had been the primary target for the expansion of built-up areas. The most extensive spatial distribution of land conversion from agriculture to built-up area was concentrated in the regencies of Bekasi, Karawang, and Cirebon. These findings are intended to provide stakeholders with enrichment in terms of available literature and with valuable inputs useful for identifying better urban and regional planning policies in Indonesia and similar regions.

With a population of 267 million, Indonesia faces the significant challenge of inaccurate rice production data leading to a flawed national rice import policy and supply problems. Its 2018 rice production and harvest area data were generated through the Area Sampling Frame (ASF) method which incurred high labour and financial costs as well as failing to optimize accuracy; hence, an alternative method needs exploration. This study compares ASF and remote sensing Synthetic Aperture Radar (SAR) methods to calculate rice growth stages (RGS) using Indramayu Regency, the highest rice producer in West Java Province, as the study area. The SAR-based method used time-series of Vertical Horizontal (VH) polarization of Sentinel-1A data that employed a combination of k-means clustering, hierarchical cluster analysis (HCA), a visual interpretation and support vector machine (SVM) classifier. Both SAR and ASF methods can generate results on a monthly basis, although remote sensing satellite time revisits can be shortened (every 12 days). Whilst the ASF, a basic technique for collecting agricultural statistics, was easy to implement in large-scale areas its accuracy depended on the quantity and representativeness of the samples. This study applied the ASF by simulating a sample size of 1.7%, 3.3% and 5% of a rice field area with unmanned aerial vehicles (UAVs) data as a reference. Whilst remote sensing SAR methods involve complex data processing the image classification process can be conducted automatically and cost-effectively (data and its software are free of charge). Moreover, it yields not only statistical data on RGS but also determines the spatial planting patterns and the RGS distribution at 10 m pixel resolution. This method showed more accurate results with overall accuracy of image classification of 81.89% and a kappa coefficient (κ) of 0.73. The comparative result was relatively small, i.e., 4,094.89 ha more than the ASF results (3.5% ARTICLE HISTORY

No abstract

No abstract