Jamaal Green

It is widely believed that planning employers increasingly seek “data science” skills. We analyze more than 16,000 job ads posted to the American Planning Association jobs board, applying natural language processing and computational content analysis to track demand for analytical skills. We find steady demand for foundational technical skills—especially GIS and Microsoft Excel—while demand for advanced skills is modest. However, the pace of technological change suggests planners may require deeper analytical and computational skills. Many planning programs are revisiting which competencies to prioritize as they prepare students for the labor market. We discuss how our findings align with professional and academic reporting.

Scholars recognize the importance of industrial land to innovative and inclusive urban economies but increasingly highlight the need for a regional approach to managing it. Few models exist, especially in the United States, but Portland, Oregon is a rare exception. This chapter assesses how the Title 4 Industrial and Employment Lands policy, overseen by the regional planning agency Metro as part of their 2040 Growth Framework, interacts with local industrial lands planning efforts. Looking at three cities in the Portland region, the chapter finds that Title 4 served to raise the baseline for local land use zoning to prevent the loss of industrial land but did not fully prevent the conversion of industrial land to other uses. More consequential was Metro's overall growth management function, which served to regulate where new industrial land supply could be added but did not readily account for spatial dynamics of manufacturing growth within the region. The chapter concludes that regional planning can be helpful but not a panacea, especially where land use planning and economic development functions remain disconnected.

Background: Child maltreatment is associated with multiple negative outcomes at the individual and societal levels. Children experiencing maltreatment are at greater risk of a host of negative outcomes (eg, psychological disorders, substance use, violent delinquency, suicidality, and adverse educational outcomes). Objective: This study aims to prevent and ameliorate child maltreatment by using a combination of geospatial smoothing via a risk terrain modeling (RTM) framework and counterfactual modeling to identify risky areas and determine the optimal (re)allocation of services to maximally improve maltreatment outcomes. Methods: A 3-stage process is proposed that can iteratively be applied within a collaborating jurisdiction to enable responsive and sustained achievement of identified child welfare outcomes. This process makes use of 2 analytic approaches: geospatial smoothing via an RTM framework and counterfactual modeling. RTM is a spatial analytic approach that uses spatial machine learning methods to estimate the risk of maltreatment based on previous cases of maltreatment and risk factors of the built environment provided by the participating jurisdiction. Using previously validated cases of maltreatment as our target variable (eg, substantiated claims of abuse and neglect) and violent crime data and built environment data as our primary predictor variables, we estimate a series of machine learning models to geospatially smooth the historically identified places at increased risk of child maltreatment. Areas identified as higher risk receive extensive services associated with preventing or limiting child maltreatment, such as prenatal or postnatal care, subsidized daycare, and parental counseling. We make use of counterfactual explanation modeling to optimally align service allocation to maximally improve maltreatment outcomes for future service allocations within a collaborating jurisdiction. The technique leverages a statistical model associating household-level information with maltreatment outcomes to explore combinations of services that would be predicted to achieve optimal and practical recommendations for future service allocation efforts. Constraints can be introduced to this logic, such as service availability and cost. Algorithmic fairness is also a potential consideration during aggregation, with possibilities for both measuring and balancing metrics such as “recourse fairness.” Results: As of September 2025, a participating jurisdiction is being recruited. Conclusions: This protocol sets forth a novel approach for the allocation of supportive services for families at risk of child maltreatment through geospatial smoothing via an RTM framework and the maximization of service impact through a counterfactual explanation model. Child maltreatment is an unfortunate and ubiquitous issue in the United States. This proposal builds on jurisdiction-wide public health strategies to allocate services in a data-informed fashion and further align future iterations of the allocation strategy using outcomes-based counterfactual modeling at the household level. The flexibility of the proposed methodology enables its application regardless of the collaborating jurisdiction’s preferences and constraints.

BACKGROUND Child maltreatment is associated with multiple negative outcomes at the individual and societal level. Children sufferers of maltreatment are at greater risk of a host of negative outcomes (e.g., psychological disorders, substance use, violent delinquency, suicidality, educational outcomes). OBJECTIVE In order to prevent and to ameliorate child maltreatment a combination of geospatial smoothing via a risk terrain modeling framework and counterfactual modeling are proffered here to identify risky areas and to determine optimal (re-)allocation of services to maximally improve maltreatment outcomes. METHODS A three stage process is proposed which can iteratively be applied within a collaborating jurisdiction to enable responsive and sustained achievement of identified child welfare outcomes. This process makes use of two analytic approaches: geospatial smooth- ing via a risk terrain framework and counterfactual modeling. Risk terrain modeling (RTM) is a spatial analytic approach that uses spatial machine learning methods to estimate the risk of maltreatment based on prior cases of maltreatment and risk factors of the built environment. Using prior validated cases of maltreatment, violent crime data and built environment data we estimate a series of machine learning models to geospatially smooth the historically identitied places at increased risk of child maltreatment. Areas identified as higher risk receive exten- sive services associated with preventing or limiting child maltreatment such as pre/postnatal care, subsidized daycare and parental counseling. We make use of counterfactual explanation modeling to optimally align service allocation to maximally improve maltreatment outcomes for future service allocations within a collaborating jurisdiction. This technique leverages a statistical model associating household-level information with maltreatment outcomes in order to explore combinations of services which would be predicted to achieve optimal and practical recommendations for future service allocation efforts. RESULTS The household level counterfactual recommendations described in the previous section will be aggregated into actionable recommendations. The primary actionable recommendation will directly inform future iterations of Stage 1 by providing a model-informed approach for determining eligible regions. There are several methods and strategies for aggregating the resulting household-level counterfactual service arrays. The first step is to select, for each household observation, which of the 2n service array permutations to select. The second step is to aggregate the household-level counterfactual service arrays into spatial subregions. These subregions could be the fishnet grids specified in Stage 1, or any other chosen subregion definition such as ZIP Code, census tract, or school district. The simplest method is to, for each service type, sum the number of households in the subregion that were determined to benefit from the service. Constraints can be introduced to this logic, such as service availability and cost. Algorithmic fairness is also a potential consideration during aggregation, with possibilities for both measuring and balancing metrics such as "recourse fairness". CONCLUSIONS This protocol sets forth a novel approach for the allocation of supportive services for families at risk of child maltreatment through geospatial smoothing via a RTM framework and the maximization of service impact through CEM. Child maltreatment is an unfortunate, and ubiquitous, issue in the United States. This proposal builds on jurisdiction-wide public health strategies in order to allocate services in a data-informed fashion and further align future iterations of the allocation strategy using outcomes-based counterfactual modeling at the household level. The flexibility of the proposed methodology enables its application regardless of the collaborating jurisdiction’s preferences and constraints.

The role of engineering, through infrastructure design, is known to influence safe driving behavior generally, yet the areas of possible intervention that could improve safe outcomes for teens are less understood. It is critical to identify interventions specific to teens, as they have different driving risk factors than more seasoned drivers, and motor vehicle crashes are the second leading cause of death among teens in the United States. We examined 5,210 fatal crashes from 2015 to 2019 in Ohio using Fatality Analysis Reporting System data. We employed multinomial logistic regression to associate the likelihood of fatal crashes involving younger teens (16 to 17 years old) or older teens (18 to 19 years old) relative to adults (20 years and older) with built environment, socioeconomic, and roadway characteristics at crash locations. Separating crashes into the two groups of teens allowed us to distinguish the possible impact of training, as younger teens are mandated to undergo driving education and behind-the-wheel training under Ohio’s graduated driver licensing laws. We found that compared with neighborhood socioeconomics, built environment factors were more significant predictors of the likelihood of fatal crashes involving younger teens compared with adults. Specifically, fatal crashes at locations with lower road density and more diverse land use were more likely to involve younger teens. Although these results could be related to a mix of younger teen driver behavior and exposure factors, the finding highlights that improving the safety of local roads in urban and rural settings could help stem teen driving fatalities.

In the last two decades, a variety of digital technologies have proliferated in cities. Urban planning educators must respond to this given local resources, constraints, and options. This commentary reviews curricular innovations being undertaken by planning faculty at five diverse institutions to advance pedagogy beyond analytics. Our contribution is to identify three general approaches to expand teaching on digital technologies: (1) undertake reforms within accredited planning programs, (2) develop new educational offerings, and (3) teach through engaged learning programs. We urge broader curricular innovation in the planning field to ensure the field’s relevance and impact in an increasingly technical future.

Owner Occupied housing is generally assumed to be safe and of reasonably high quality in the United States. Prior research on housing quality using the American Housing Survey generally finds housing quality is good. Recent research critiquing housing quality measures found in the AHS and offering alternative measures complicate this assumption by showing housing quality is more variable than usually assumed and the costs of housing repair needs are worth billions of dollars a year. In order to better understand the state of housing quality in owner occupied housing we present information from Philadelphia's Basic Systems Repair Program (BSRP). BSRP is an emergency repair program that offers grants to low income homeowners. From 2009-2019 the city has spect a little over $98 million in repairs helping nearly 11,000 households. Comparing AHS derived estimates of housing quality to the BSRP we find that housing quality for owner occupied homes in Philadelphia may be more severe than is often assumed and that severe housing quality issues disproportionately affect Black homeowners in the city. Planners concerned with stabilizing neighborhoods, protecting property values and the racially disparate costs of homeownership should pay closer to attention to housing quality issues and look at the feasibility of emergency home repair programs in affected areas.

An estimated one third of children in the United States will suffer from maltreatment. The use of spatial predictive analytics offers an opportunity to delineate places at elevated risk of child abuse. Risk terrain modeling is a spatial analytic framework for predicting instances of varied types of crime. This article compares a random forest negative binomial model in a risk terrain modeling framework to the question of predicting counts of substantiated child abuse in Portland, Oregon. The final model specification includes domestic incident data from the Portland Police Bureau, built environment data from the City of Portland, OpenStreetMap data, and a neighborhood deprivation index derived from American Community Survey data predicting counts of substantiated child maltreatment from the Oregon Department of Human Services administrative data. The random forest outperforms the negative binomial model, showing its superiority in a risk terrain modeling framework, though the relative lack of predictive importance of the built environment variables compared to the domestic incident neighborhood deprivation variables should encourage researchers to further investigate the role of the built environment in the problem of predicting child abuse and maltreatment.