Geospatial Tools Research Articles

OSGEO, PERSISTENT IDENTIFIERS AND THE SHAPE OF THINGS TO COME

Abstract. The Open Source Geospatial Foundation (OSGeo) is an international community, which maintains and develops a growing number of quality-assured open source geospatial software tools. OSGeo supports Open Science and the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The open source software projects of OSGeo are drivers for innovation, but also benefit from technical advances made in other fields to foster FAIR. We report on the of adoption of persistent identifiers (PID) by OSGeo communities, which is accelerating and diversifying. Persistent referencing by PID was introduced to OSGeo for scientific video in 2014. Since 2017 it is being adopted by a growing number of OSGeo software projects to enable scientific citation of software repositories, individuals software releases and involved persons. This enables tangible scientific credit for OSGeo volunteer work and is expected to affect OSGeo workflows, such as the incubation and graduation process. For illustration, examples from the OSGeo projects GRASS GIS and actinia are provided.

AN OPEN-SOURCE MOBILE GEOSPATIAL PLATFORM FOR AGRICULTURAL LANDSCAPE MAPPING: A CASE STUDY OF WALL-TO-WALL FARM SYSTEMS MAPPING IN TONGA

Abstract. Pacific Island Countries (PICs) such as Tonga rely on services provided by agricultural landscapes to support livelihoods, economic activity, and food security. At the same time these landscapes face numerous pressures and risks from factors such as environmental, climate, and market changes. Accurate, spatially explicit, and timely datasets on agricultural systems is required for an array of land and agricultural management tasks. Here, the development of an open-source ICT system providing geospatial tools for landscape monitoring, developed in collaboration between geospatial researchers and Tonga’s Ministry of Agriculture, Food, and Forests (MAFF), is presented. The agile and iterative ICT for Development (ICT4D) framework used to elicit MAFF’s requirements for the ICT system is presented alongside the system architecture and case studies demonstrating its impact. A key goal of the ICT4D development process was to develop an ICT system to support MAFF from transitioning from infrequent, paper-based farm surveys to coordinated, large-team, spatially explicit digital surveying augmented by tools for analysis and reporting. The mature system architecture which includes QField and QFieldCloud, and new open-source geospatial components for spatial visualisation, analysis, and reporting is presented. Case studies where the mature tool was used by MAFF’s are presented and include: (1) how a large survey team captured spatial data for >11,000 farms for country-wide farm monitoring; and (2) how the tool informed MAFF’s landscape decision making including recovery efforts after the 2022 Hunga Tonga–Hunga Ha’apai volcano explosion. The success of the tool demonstrates the importance of stakeholder engagement and the great potential for open-source geospatial tools for landscape management and disaster response in PICs.

Integrated approach of ecosystem services for mine reclamation in a clustered mining semi-urban region of South India

Ecosystem service gets more attention among the environmental policy-makers in large-scale industries, especially in the mining industry. The service may direct or indirect benefit to people in various ways. The millennium ecosystem assessment typology is used to appraise the impact on ecosystems, and human well-being, and the ecosystem services are divided into four types as provisioning, regulating, supporting, and cultural services. We selected Ariyalur district of South India as a study area because of the clustered mining region. This study helps to trace the relationship between mining and ecosystem function. Though mining industry serves the provisioning of raw materials, the mining operations impose the negative impacts on environment of that region. The focus of the present study is to estimate the values of ecosystem service of Ariyalur district using benefit transfer method with reference to LULC change over the past 20 years from 2000 to 2020. To attain the proposed framework, the spatial distribution of geographic features of the study area was developed using geospatial tools. The study estimated the value of INR 83.29 billion of ecosystem services contributes to humans directly or indirectly in the year of 2020. In addition to this, the study determined the correlation function between ecosystem service values and LULC changes. The study suggested a measure to enhance the existing ecosystem services of the region in approach to land suitability analysis for mining site reclamation plan using entropy weight method.

Relative assessment of activated carbon and Nano-Material based adsorbents used for obliteration of THMs precursors - regeneration and techno-economic feasibility analysis

• Geo-spatial diversity of THMs was investigated in Indian drinking water. • The performances of CONP and AM-BFS are compared through detailed simulations. • CONP possesed maximum sorption (94%) of THMs precursors than AM-BFS. • CONP showed excellent reusability (97.40 %) upto 5th consecutive cycles. • CONP found relatively cheaper (20–50%) than commercial PAC. The study presents the relative assessment of activated carbon and Nanomaterial-based adsorbents used to eradicate trihalomethane (THMs) precursors. The spatial distribution of total THMs (TTHMs) in Indian drinking water was established using Geospatial analytical tools [ArcGIS 10.3]. Principal Component Analysis (PCA) was applied to identify the significant principal THM species. The physicochemical properties of adsorbents, i.e., Acid Modified Beal Fruits Shell (AM-BFS) and Cerium Oxide Nano-Particles (CONP), were investigated by high-resolution transmission electron microscope (HRTEM) with energy dispersive X-ray (EDX), Brunauer–Emmett–Teller (BET) surface area, and zeta (ζ) potential. HRTEM study revealed that both the particles were spherical and randomly distributed. The value of average pore diameter (AM-BFS – 120.68 Å, CONP – 77.88 Å) confirmed the mesoporous nature of adsorbents. The color overlap map of EDX analysis indicated percentage progression in all the respective elements present in both materials after the adsorption process. Regeneration of adsorbents using HCl was more effective than the organic solvents (acetone, methanol) and deionized water (DW). Response surface methodology (RSM) was used to predict the regeneration ability of the exhausted adsorbents. The techno-economic feasibility analysis suggested that CONP is more effective and economical than AM-BFS for removing THMs precursors from drinking water.

SiteFinder: A geospatial scoping tool to assist the siting of external water harvesting structures

Water harvesting has a long history, but still plays an important role today by increasing crop productivity, combatting erosion, and improving water supplies. Geographical Information Systems (GIS) are used extensively to assess the suitability of sites for water harvesting but available tools fail to consider the synoptic topography of sites. Here, we report the creation of a novel, automated tool – “SiteFinder” – that evaluates potential locations by automatically calculating site-specific information, including structure parameters (height, length, and volume) and descriptors of the zone affected by the structure (storage capacity and area of influence) and the catchment area. Innovatively, compared to existing tools of this kind, SiteFinder works within a GIS environment. Thus, it allows the possibility of combining its outputs with larger Multi-Criteria Decision-Making processes to consider other bio-physical, socio-economic, and environmental factors. It utilises a Digital Elevation Model (DEM) and automatically analyses thousands of potential sites, computing site characteristics for different barrier heights that are dependent on the surrounding topography. It outputs values of eight parameters to aid planners in assessing the characteristics of sites as to their suitability for water harvesting. We conducted case studies using 30 × 30 m gridded DEMs to automatically evaluate several thousand sites and, by filtering the tool outputs, successfully identified sites with characteristics appropriate for scenarios at three spatial scales: large dams for nationally significant water supply reservoirs (383 sites analysed; 5 filtered sites with barriers up to 30 m in height); large gully erosion control dams for regional-scale interventions (4,586 sites analysed; 6 filtered sites with barriers up to 3.6 m in height); and local, community-based earth embankment projects (801 sites analysed; 6 filtered sites with barriers up to 2 m in height). A higher resolution (1 × 1 m) terrain elevation model, derived from open-source airborne survey data, was used to assess the veracity of these results. Correlations between the barrier length, impounded area and storage volume capacity derived from the two different resolution data sets were all strongly significant (Spearman’s rank correlation, p < 0.001); and normalised root mean square errors were 9%, 15% and 16% for these parameters, respectively.

Spatio-temporal analysis of shoreline changes and erosion risk assessment along Jerba island (Tunisia) based on remote-sensing data and geospatial tools

Jerba, the largest island in North Africa, is among the most exposed islands to the combined effects of climate change and human activities in the Mediterranean. The eastern coast of Jerba island has long been subject to intense tourist development due to its attractive natural potential. The main issue addressed in this study concerns the evolutionary trends of Jerba shoreline and the assessment of coastal erosion risk. This work focuses on the historical study of the shoreline positions based on satellite data processing. The use of geographic information system (GIS) and the Digital Shoreline Analysis System (DSAS) geostatistical calculation tool allowed for the identification of erosion and accretion rates and the description of evolutionary trends across 32 years covering three periods (1989–1999, 1999–2009, and 2009–2021). The results show an overall erosion trend (89% of the coastline). The statistics revealed that the study area, with the exception of the sandy spit, has experienced an intense and alarming level of erosion between 1989 and 1999, with an end point rate (EPR) equal to −4.12 m/year. This net rate continued to be negative in the period between 1999 and 2009, with an EPR of −0.5 m/year. Between 2009 and 2021, the net rate of shoreline change is marked by slight accretion, at an average of +1.4 m/year. The overall long-term trend shows the shoreline is eroding, with an average linear regression rate of −1.38 m/year for the whole period. This trend is due to a combination of the cumulative effects of sea-level rise and anthropogenic actions. Therefore, sustainable management strategies need to be implemented urgently by coastal managers to restore the sectors that are susceptible to further erosion.

Integrated geophysical, geochemical, and geospatial tools to characterize water resources in GAIE, Eastern Peshawar basin, Pakistan

Integrated geophysical, geochemical, and geospatial tools to characterize water resources in GAIE, Eastern Peshawar basin, Pakistan

Impact of coal mining on land use dynamics and soil quality: Assessment of land degradation vulnerability through conjunctive use of analytical hierarchy process and geospatial techniques

AbstractCoal is the major source of energy in India, where 66% of the thermal power is generated from different grades of coal. The surging energy demand for domestic and industrial sectors has accelerated coal production through intensive coal mining from existing mines and there is exploration for potential areas across the Country. More than 80% of the coal excavated from open cast mining, which is a damaging form of mining that is often associated with undesirable land use changes, soil degradation, and deforestation and is responsible for other environmental degradation. The present study has been conducted in Manendragarh coal mining area located in northern Chhattisgarh, Central India using the analytical hierarchy process (AHP) and geospatial tools. LANDSAT‐TM and OLI satellite data from 1990 and 2020 were digitally analyzed applying a maximum likelihood supervised classification technique in ERDAS IMAGINE and Arc‐GIS platforms. Spatial analysis of satellite data revealed that the area under forest, agriculture‐fallow, and waterbodies fell by 14.5%, 12.9%, and 2.52%, respectively, due mainly to conversion into degraded land, while the mining area and built‐up areas were proportionally enhanced by 3.2% and 0.59% between 1990 and 2020. The intense mining resulted in deterioration of soil health, where the organic C, pH, and soil depth were considerably decreased in mined sites and degraded lands, compared to agricultural and forest areas. The major determinant biophysical, topographic, and soil factors were integrated in a GIS platform to derive a land degradation vulnerability index (LDVI), which identified five vulnerability classes. The salient findings of study provides the critical inputs for the policy makers/environmentalists in prioritization and decision‐making for implementing remedial measures for the eco‐restoration of mining degraded landscapes and also the improvement of ecology and environment along with sustainable coal mining.

Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning

Identifying suitable watersheds is a prerequisite to operationalizing planning interventions for agricultural development. With the help of geospatial tools, this paper identified suitable watersheds across Nigeria using biophysical parameters to aid agricultural planning. Our study included various critical thematic layers such as precipitation, temperature, slope, land-use/land-cover (LULC), soil texture, soil depth, and length of growing period, prepared and modeled on the Google Earth Engine (GEE) platform. Using expert knowledge, scores were assigned to these thematic layers, and a priority map was prepared based on the combined weighted average score. We also validated priority watersheds. For this, the study area was classified into three priority zones ranging from ‘high’ to ‘low’. Of the 277 watersheds identified, 57 fell in the high priority category, implying that they are highly favorable for interventions. This would be useful for regional-scale water resource planning for agricultural landscape development.

Identification of contributing factors and hotspots of pedestrian-vehicle collisions in urban areas: An alternative hybrid method for small cities

Objective Traffic safety numbers in developing countries show a serious global problem that requires studies and actions to promote safety for the most vulnerable users, such as pedestrians, as a way of contributing to sustainable urban mobility. Therefore, this research proposes a hybrid method to identify contributing factors and critical points according to the profile of crashes involving pedestrians being run over by motor vehicles in small cities. Methods The proposed method uses multivariate data analysis, specifically Exploratory Factor Analysis (EFA) and Cluster Analysis (CA), combined with geospatial tools. The study scenario corresponds to pedestrian-vehicle collisions that occurred in the urban area of Cachoeira do Sul, a small city in Rio Grande do Sul, Brazil, recorded in police reports between 2015 and 2018. Additionally, Geographic Information Tools (GIS) were used to collect characteristics of the road environment. Results According to EFA, a set of attributes configured as contributing factors are discussed according to the components of the traffic system: user, road, vehicle, and environment. Subsequently, a CA was applied with the road-environmental factors, and three pedestrian-vehicle collision profiles were extracted, with clustering according to road segments with homogeneous characteristics. Moreover, a Kernel Density Estimate (KDE) was used to obtain heat maps of the pedestrian collisions profiles. The results allowed interventions to be proposed according to the contributing factors of each profile. Conclusions The multivariate statistical analysis supported the discussion of the factors contributing to the occurrence of pedestrian-vehicle collisions in small cities in developing countries. The spatial visualization of the results showed urban corridors with a high risk for pedestrians and the identification of hotspots in the city. In addition, the study demonstrated that the hybrid method tested is an alternative for the government of cities with few resources to promote safety mobility.

Учет влияния вариаций гравитационного поля в геодезии современными средствами геопространственного анализа

The article deals with assessment of the Earth`s gravity field variations (GFV) affecting the repeated leveling results using modern geospatial analysis tools in the study of natural and engineering objects’ deformations. The basis of the calculation is put in the well-known formula that connects reference level of the point displacement with potential of gravity variation on it, caused by the volume of the redistributed surrounding masses. The desired change in the potential is determined by the method of numerical integration through the sum of elementary masses redistributed volume’s contributions to it. At estimating we used the possibility of obtaining the most reliable digital model of various landforms in the ArcGIS software environment and determining the dimensions of a complex body with a fairly high accuracy. In this case, the advantage of the rectangular partition into elementary volumes of the quasi-cone layer base, which is close in geometry to the shapes of igneous or man-made rocks model over the radial one is shown. The developed method for calculating the impact of GFV on leveling using the capabilities of ArcGIS is fully automated through compiling the corresponding script in python. Specific experimental material shows that it is possible to assess the above mentioned impact with any detailing

Mapping potential risks for the transmission of spotted fever rickettsiosis: The case study from the Rio de Janeiro state, Brazil.

Spotted fever rickettsiosis is a zoonosis transmitted by ticks, having a varied clinical course that can lead to death if not managed properly. In Brazil it is more commonly observed in the Southeast, being an emerging public health problem. Hazard mapping models are common in different areas of knowledge, including public health, as a way of inferring reality and seeking to reduce or prevent damage. The aim of this study is to offer a spatial heuristic methodology for assessing the potential risk of transmission of spotted fever in the Rio de Janeiro state, located in the southeastern region of Brazil. For this, we used geospatial tools associated with eco-epidemiological data related to the clinical profile of the disease. The results achieved were substantially encouraging, considering that there are territories with greater or lesser expectation of risk for spotted fever in the study area. We observed that there are important distinctions between the two rickettsiosis scenarios in the same geographic space and that the areas where there is a greater potential risk of contracting rickettsiosis coincide with the administrative regions that concentrated the cases of hospitalization and deaths from the disease, concluding that the scenery found are relevant to the case series for the disease and that the planning of surveillance actions can gain in quality if the use of this spatial analysis tool is incorporated into the routine of local health management.

Applications of Sentinel-2 satellite data for spatio-temporal mapping of deep pools for monitoring the riverine connectivity and assessment of ecological dynamics: a case from Godavari, a tropical river in India (2016-2021).

Rivers are dynamic ecosystems with diverse habitats that require ample connectivity to ensure the flow of ecosystem services, thus empowering the sustainable development of an entire basin. Geo-spatial tools offer powerful prospects for monitoring of aquatic ecosystems. The usefulness of Sentinel-2 datasets to assess river connectivity has been explored for an un-gauged seasonal river system. The present study was undertaken in five ecologically unique river reaches viz. Wainganga, Wardha, Pranhita, Godavari-mid and Manair in Godavari Basin in the Indian Deccan Plateau to map water spread dynamics at various time scales, i.e.,fortnightly, monthly, seasonal, annual and demi-decadal during 2016-2021. The maximum value of perennial water spread per square kilometre of total floodplain area (2016-2021), determined using Sentinel-2 imageries, was observed in river Wardha (0.18) followed by Pranhita (0.12) and Wainganga (0.11). The water spread showed a decreasing trend, while the number of patches in the river corridor increased over time from post-monsoon to pre-monsoon season. The copious perennial habitat with relatively larger patches, incessant flow in river Pranhita and obstructed flow, large-sized patches reported in river Wardha during summer months, hold importance in terms of providing refuge to aquatic biota. This study provides evidence for the impact of water projects on spatio-temporal water spread dynamics in Godavari Basin. The demonstrated utility of Sentinel-2 imagery coupled with gauge station measurements for river continuity assessment and deep pool mapping would aid in enhancing our understanding on environmental flow at a spatial scale, which in turn would aid in effective river management to achieve the Sustainable Development Goals. The implications of this study for sustainable environmental management and limitations are also discussed.

Climate change impact on shrimp (Litopenaeus vannamei) farming in Banyuwangi, East Java

Climate change negatively impacts the lives and livelihoods of humans, especially in coastal area. Shrimp farming is one of the activities in this area. Banyuwangi is the largest shrimp producers in East Java. The aim of this research is to know the impact of climate change on shrimp (Litopenaeus vannamei) farming in Banyuwangi, East Java. The present study was carried out (from June to July 2020). Focused area on Wongsorejo, Kalipuro, Kabat, Blimbingsari, Banyuwangi, Tegaldlimo and Muncar. The study was conducted using both primary and secondary data sources. Data collection techniques with survey and interview, and the instrument are questionnaire. Secondary data sources from literature and data from GIS. Analysis of the collected data was carried out using SPSS or an excel sheet, and geospatial tools (ArcGIS). The study shown that the climate change has an adequate effect on shrimp farming activities. This can be seen from the high percentage of production failures during the rainy season. Fluctuations in water quality also often occur in shrimp ponds. In addition, the incidence of disease sporadically appears in some ponds.

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the hybrid extraction and clustering of social events from big social data streams. We rely on a hybrid learning model, where supervised deep learning is used for feature extraction and topic classification, whereas unsupervised spatial clustering is employed to determine the event whereabouts. We present ‘Deep-Eware’, a scalable and efficient event-aware big data platform that integrates data stream and geospatial processing tools for the hybrid extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. The system integrates a semantic keyword generation tool using KeyBERT for dataset preparation. Event classification is performed using CNN and bidirectional LSTM, while hierarchical density-based spatial clustering was used for location-inference of events. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that this hybrid approach for spatio-temporal event extraction has a major advantage for real-time spatio-temporal event detection and tracking from social media. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs.

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the incremental extraction and clustering of social events from big social data streams. We present ‘E-ware’, a scalable and efficient big data platform that integrates data stream and geospatial processing tools for the incremental extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. Our incremental clustering technique employs temporal sliding windows, in order to update the discovered topic clusters with the upcoming social streams (i.e., tweets). The system integrates an efficient spatio-temporal index for fast retrieval and updates of evolving event clusters. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that E-ware has a major advantage for real-time incremental detection and tracking of events, both spatially and temporally. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

Predicting and Scoring Estuary Ecological Health Using a Bayesian Belief Network

Excessive nutrient and sediment inputs threaten ecological condition in many estuaries. We describe a Bayesian Belief Network (BBN) that calculates an Estuary Trophic Index (ETI) score ranging between 0 (no symptoms of eutrophication) to 1 (grossly eutrophic) for estuaries in Aotearoa New Zealand (NZ). The ETI BBN includes estuary physiographic characteristics (estuary type, flushing time, intertidal area, estuary closure state, water column stratification) and nutrient and sediment loads available from existing geospatial tools and databases, that drive responses of ‘primary’ indicators (macroalgae and phytoplankton biomass) and ‘secondary’ indicators (or symptoms) of estuary ecological impairment (sediment carbon, sediment apparent redox potential discontinuity depth, water column oxygen, macrobenthos and seagrass condition). Relationships between the BBN nodes are based primarily on observational and model-based information from NZ and international studies rather than expert opinion. The model can be used in a purely predictive manner under knowledge-poor situations, using only the physiographic drivers and nutrient/sediment loads, or refined using field-derived observations of indicator values to reduce the uncertainty associated with the probabilistic BBN score. It is designed for shallow tidal lagoons, tidal river estuaries and coastal lakes; systems which are sensitive to eutrophication and sedimentation pressure and are common in NZ and globally. Modelled ETI BBN scores agreed well with ETI scores calculated from observed indicator values for 11 well-studied NZ estuaries. We predict ecological condition of 291 NZ estuaries, most of which have no monitored information on trophic state. We illustrate capabilities of the ETI BBN with two case studies: to evaluate improvements in estuary health arising from diversion of wastewater from an estuary via an ocean outfall, and to estimate catchment diffuse nutrient load reductions required to meet estuary health objectives. The ETI BBN may serve as a template for other agencies wishing to develop similar tools.

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Planning for Sustainable Cities in Africa: Experiences, Challenges and Prospects of Monitoring Geospatial Indicators

The African continent is receiving unprecedented pressure from population growth, urbanisation, decreased agricultural productivity and changing climate. However, the continent lacks technological advancement. Therefore, there is a need to apply global data and open geospatial tools for analysis to prevent, stop and comprehend the trend and effects of land degradation, food insecurity and the unsustainability of cities. The study takes three representative indicators (climate risk, land degradation and land consumption) from FAO’s four strategic better’s to demonstrate the feasibility and applicability of global datasets to support decision makers. Three representative cities in Africa are selected for the study—Houet, Burkina Faso (West Africa); Kisumu, Kenya (East Africa); and Analamanga, Madagascar (South East Africa). The study found that eight Fokontany of the Analamanga region were at high risk from climate change; at the ward level, a maximum of 54.2% of the total degraded land area in Kisumu; and maximum land-consumption rate of 1.5 was found in Houet at the department level. The results of this study can be a basis for policymakers in planning an inclusive climate-adaptation measure and sustainable land-use frameworks and policies.

Advancing a hyperlocal approach to community engagement in climate adaptation: Results from a South Florida pilot study in two communities

With increasing urgency of local and regional climate adaptation, there is a growing need for governments to identify and respond effectively to the concerns of communities they serve and to align investments. We designed and piloted a novel hyperlocal method for urban adaptation planning combining two social science tools that have been widely but separately used to foster community engagement and strategize solutions. Our not-for-profit community partners facilitated multi-session online workshops with participants from two communities in South Florida with whom they have well-established relationships and in which socio-economic conditions and climate risks represent notable vulnerabilities. The workshops first employed photovoice to elicit individual narratives about climate change impacts; participants then followed a design thinking protocol to critically evaluate the leading concerns they identified and propose adaptation solutions. Geospatial mapping and data tools were provided for participants to gain additional tools and further knowledge. Local planning and resilience officials attended some or all of the workshops as observers and interlocutors, dialoguing with participants. Comparative analysis revealed differences in risk awareness and primary concerns between communities, and further demonstrated that concerns and solutions proposed by members of at-risk neighborhoods do not always align with geospatial data that often drives infrastructure adaptation planning in the region, suggesting that more widespread use of community engaged methods could enhance government climate adaptation responses for local communities.