The making of an uneven shore: How coastal management and housing policies shaped racial inequality in asbury park NJ

Recent work in urban geography and political ecology has explored the roots of housing segregation in the United States within governmental polices and racial prejudice within the real estate sector. Additional research has demonstrated how coastal management practices has largely benefited wealthy, white communities. In this paper, I bring together insights from these two strands of research to demonstrate how both coastal management and governmental housing policies combined to shape racial inequalities within and around Asbury Park, New Jersey. By focusing on the period between 1945 and 1970, I show how local, state, and federal actors repeatedly prioritized improving and protecting the beachfront areas of the northern New Jersey shore while promising to eventually address the housing and economic needs of the predominately Black ‘West Side’ neighbourhood of Asbury Park. This paper demonstrates that not only did governmental spending on coastal management largely benefit white suburban homeowners but also came at the expense of promised spending within Black neighbourhoods. The case study has implications for other coastal regions in the United States in which housing segregation persists. As climate change and sea level rise unfold, the history of racial discrimination in coastal development raises important considerations for efforts to address emerging hazards and risks.

Impacts of Climate Change on Coastal Communities

Earth and coastal ecosystems are not static, and they usually respond to environmental changes, mostly anthropogenic and climatic. Here, the authors described natural values, coastal landforms, and types of infrastructure that are most likely to be affected by climate change (CC) and provide information for assessing inundation, erosion, and recession risks for a chosen location. In this chapter, the authors focused on the land uses, the vulnerability of coastal infrastructure, and argued for effective linkages between CC issues and development planning. They also recommended the incorporation of CC impact and risk assessment into long-term national development strategies. Policies will be presented to implement these recommendations for adaptation to climate variability and global CC. The authors provide general recommendations and identify challenges for the incorporation of climate change impacts and risk assessment into long-term land-use national development plans and strategies. Overall, this chapter provides an overview of the implications for CC to coastal management.

The Effect of Human Activities Towards Coastal Dynamics and Sustainable Coastal Management

One of the disasters that often occur in coastal areas is abrasion. Abrasion causes coastal dynamics, including the East Coast of Rembang, Kragan Village, Kragan District, Rembang Regency. From 1975 to 1990, at least 50 meters of land from this area has been lost due to abrasion. This dynamic may become one of the causes of unsustainable management of the coastal environment and its natural resources. Various efforts have been made to overcome abrasion, but abrasion continues to hit this area, even until 2020. Qualitative and quantitative approaches were carried out in this study to discover the coast dynamics and various human activities that may trigger abrasion. Image interpretation, observation, interviews, and questionnaires were used as data collection techniques at three observation points in the Kragan Village area. This study concludes that the beach in Kragan Village has experienced dynamics with a total land loss of 46 meters from 2003 to 2020. Harmful activities carried out by humans resulted in abrasion so that the coast experienced dynamics. Human activities also affect coastal management, namely the basic principles of integrated coastal management and processes in the management of coastal areas.

Citizen-Science with off-the-shelf UAV for Coastal Monitoring

Accurate and repetitive observation and quantification of the shoreline position and the coastal feature are essential aspects of coastal management and planning. Commonly, the dataset associated with coastal observation and quantification is obtained with in-situ coastal surveys. The current methods are mostly quite expensive, time-consuming, and require trained individuals to do the task. With the availability of the off-the-shelf low cost, lightweight, and reliable Unmanned Aerial Vehicle (UAV) with the advances of the algorithms such as structure-from-motion (SfM), UAV-based measurement becomes a promising tool. Open SfM initiative, open topographical database, and UAV communities are the enablers that make it possible to collect accurate and frequent coastal monitoring and democratize data. This paper provides a review and discussions that highlight the possibility of conducting scientific coastal monitoring or collaborating with the public. Literature was examined for the advances in coastal monitoring, challenges, and recommendations. We identified and proposed the use of UAV along with the strategies and systems to encourage citizen-led UAV observation for coastal monitoring while attaining the quality.

Analysis of accuracy comparison tidal global (FES2014, TPXO9) and regional (BIG Prediction) models to the existing tides in Surabaya and surrounding waters

Tidal data has a significant role in various fields in hydrographic surveys and navigation, port planning, and other coastal management. The number of fixed tide stations in Indonesia is minimal compared to the vast territorial waters in Indonesia. So that for areas that are not covered by tidal fixed stations, direct tidal observation with a certain length of observation is necessary, and of course, this requires quite expensive costs. Fortunately, there are regional and global tidal data predictions that can be used to determine tidal conditions in Indonesian waters. In this study, the regional (BIG) and the global (FES2014 and TPXO9) tidal data prediction models were validated with direct observation in the five locations such as Surabaya, Gresik1, Gresik2, Bangkalan, and Giligenting for 39 hours. The root means square error (rmse) calculation results show that in the five locations, the BIG tidal prediction has the smallest rmse value in three tidal stations at Gresik 1, Gresik 2, and Gili Genting with 0.303 m, 0.050 m, and 0.155 m respectively. At the same time, the TPXO9 tidal model shows the biggest rmse at Gresik 1, Gresik 2, and Bangkalan with 0.420 m, 0.195 m, and 0.630 m, respectively.