Dataset Information

Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm.

ABSTRACT: In the complex process of urbanization, retrieving its dynamic expansion trajectories with an efficient method is challenging, especially for urban regions that are not clearly distinguished from the surroundings in arid regions. In this study, we propose a framework for extracting spatiotemporal change information on urban disturbances. First, the urban built-up object areas in 2000 and 2020 were obtained using object-oriented segmentation method. Second, we applied LandTrendr (LT) algorithm and multiple bands/indices to extract annual spatiotemporal information. This process was implemented effectively with the support of the cloud computing platform of Earth Observation big data. The overall accuracy of time information extraction, the kappa coefficient, and average detection error were 83.76%, 0.79, and 0.57 a, respectively. These results show that Karachi expanded continuously during 2000-2020, with an average annual growth rate of 4.7%. However, this expansion was not spatiotemporally balanced. The coastal area developed quickly within a shorter duration, whereas the main newly added urban regions locate in the northern and eastern inland areas. This study demonstrated an effective framework for extract the dynamic spatiotemporal change information of urban built-up objects and substantially eliminate the salt-and-pepper effect based on pixel detection. Methods used in our study are of general promotion significance in the monitoring of other disturbances caused by natural or human activities.

SUBMITTER: Yan X

PROVIDER: S-EPMC8632927 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundData collection is the most critical stage in any population health study and correctly implementing fieldwork enhances the quality of collected information. However, even the most carefully planned large-scale household surveys can encounter many context-specific issues. This paper reflected on our research team's recent experience conducting surveys for a quasi-experimental evaluation of a reproductive health program in urban areas of Karachi, Pakistan. We aim to describe the issues encountered and lessons learned from this process, and present some potential solutions for conducting future household surveys in similar urban environments.MethodsThe study followed a three-stage random sampling design. Initially, a Geographical Information System (GIS) was used to construct the sampling frame with union council (UC) area mapping and cluster demarcation followed by random selection of clusters in the selected UCs within the intervention and control sites. The second stage involved a complete household listing in selected clusters and the final stage was a random sampling of households with eligible women.ResultThis paper describes the issues that were encountered including technical problems related to GIS demarcation of cluster boundaries and hand-held devices for computer assisted personal interviews (CAPI), household listing, interviewing respondents on sensitive topics and their expectations, and ensuring privacy during the survey.ConclusionThis study identifies a number of unique barriers to conducting household surveys in Karachi and highlights some key lessons for survey research in urban settlements. GIS mapping technology is a cost-effective method for developing sampling frames in resource-constrained settings. Secondly, the strategy of interviewing women immediately after the cluster is listed may be applied to make it easier to re-locate selected respondents and to reduce loss-to-follow up. Understanding local norms and developing culturally appropriate strategies to build trust with communities may significantly improve survey participation. Researchers should hire experienced female enumerators and provide continuous training on best practices for interviewing women on sensitive reproductive health topics in urban communities.

Project description:ObjectiveTo determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults.MethodsWeight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values.ResultsBuilt environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg.ConclusionsBuilt environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

Project description:Low- middle-income countries, including Pakistan, are facing significant obstacles in their efforts to achieve the global targets for maternal, newborn, and child health (MNCH) defined by the Sustainable Development Goals. Barriers at the individual, health system, and contextual levels undermine healthcare access for pregnant women and children, disproportionately affecting those in low-resource settings. To address these challenges in the high-mortality, peri-urban areas of Karachi, VITAL Pakistan Trust and Aga Khan University launched a digital health intervention (DHI) to stimulate demand for health services and streamline care management for health workers at the primary care level. In this case study, we present a narrative review of the design, development, and deployment of the DHI, an Android-based application, in accordance with the Principles for Digital Development. We draw on the initial experience with implementation to reflect on how each of the nine Principles was considered during different phases of the project lifecycle, focusing on the lessons learned and challenges encountered during this process. By engaging with end-users and understanding the community, we were able to map existing relationships and workflows onto a digital platform to address major challenges hindering service delivery. Leveraging insights from field observations and user feedback, we collaborated with experts in healthcare and technology to develop the DHI, which has now scaled to 44 peri-urban settlements in Karachi. Our experience underscores the value of substantiated frameworks like the Principles. However, on-ground challenges reveal important caveats requiring further assessment. These include building community trust in new digital systems and ensuring the ethical use of health data, particularly in low digital and data literacy contexts. Based on this understanding, we share recommendations for conditions central to the effective integration and uptake of technology in healthcare, specifically within the context of digital health for MNCH.

Project description:BACKGROUND: Secondary infertility in developing countries is mostly attributable to blockage of the fallopian tubes due to adhesions caused by reproductive tract infections. There is a dearth of information on the prevalence and causes of secondary infertility from Pakistan. This paper presents results on factors associated with secondary infertility among married women in Karachi, Pakistan. METHODS: A matched case-control study was conducted. Cases were women aged 15-35 years with history of at least one previous conception and currently seeking treatment for secondary infertility. Controls were women residing in the neighborhood of cases with at least one live birth and not taking treatment for secondary infertility. The age of controls was matched by ±5 years to that of cases. Data was collected from June to August 2003. Conditional logistic regression was used to determine crude and adjusted odds ratios (OR) with corresponding 95% confidence intervals (CI) for factors associated with secondary infertility. RESULTS: The final multivariate logistic regression model revealed that after adjusting for age, cases were more likely to be the housewives (AOR = 2.6, 95% CI:1.5-4.4), had used inappropriate material to absorb blood during menstruation (AOR = 9.0, 95% CI: 5.0-16.4), and at their last delivery, had a birth attendant who did not wash hands with soap and water (AOR = 3.0, 95% CI: 1.4-5.7). Moreover, women with secondary infertility were more likely to report current or past history of having STI symptoms (AOR = 3.6, 95% CI: 2.4-5.6) and use of intra-vaginal indigenous medicines during their last post-partum period (AOR = 3.1, 95% CI: 1.6-5.7). CONCLUSION: We recommend health education and awareness messages for safe practices during menstruation, delivery, and the postpartum period for women in general. Additionally, sanitary napkins should be made available at an affordable cost, and safe delivery kits should contain educational/pictorial brochures for appropriate hand washing skills.

Dataset Information

Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets